增加mla注意力模型
baihaochen
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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MiniMind is an ultra-small language model (~26M-104M params) designed for training from scratch on consumer GPUs. It demonstrates the complete LLM training pipeline at minimal cost. The codebase is a Chinese-language educational project with English code.
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## Common Commands
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### Training (all run from `trainer/` directory)
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```bash
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# Stage 1: Pretraining
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cd trainer && torchrun --nproc_per_node=1 train_pretrain.py
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# Stage 2: Supervised Fine-Tuning
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cd trainer && torchrun --nproc_per_node=1 train_full_sft.py
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# Optional: LoRA / DPO / PPO / GRPO / Agent RL / Distillation
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cd trainer && torchrun --nproc_per_node=1 train_lora.py
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cd trainer && torchrun --nproc_per_node=1 train_dpo.py
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cd trainer && torchrun --nproc_per_node=1 train_ppo.py
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cd trainer && torchrun --nproc_per_node=1 train_grpo.py
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cd trainer && torchrun --nproc_per_node=1 train_agent.py
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cd trainer && torchrun --nproc_per_node=1 train_distillation.py
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```
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Multi-GPU: change `--nproc_per_node=N`. Resume training with `--from_resume 1`.
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### Inference
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```bash
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python eval_llm.py # CLI inference
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python eval_llm.py --from_transformers # Load transformers-format model
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python eval_llm.py --lora_path <path> # With LoRA adapter
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python scripts/serve_openai_api.py # OpenAI-compatible API server
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```
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### Model Conversion
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```bash
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python scripts/convert_model.py # torch <-> transformers format, LoRA merge
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```
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### Dependencies
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```bash
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pip install -r requirements.txt
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# PyTorch must be installed separately (commented out in requirements.txt)
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```
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## Architecture
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### Training Pipeline (sequential)
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`Pretrain` → `Full SFT` → optional (`LoRA` / `DPO` / `PPO` / `GRPO` / `Agent RL` / `Distillation`)
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### Model (`model/model_minimind.py`)
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- `MiniMindConfig`: HuggingFace PretrainedConfig with hidden_size=768, 8 layers, vocab_size=6400
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- `MiniMindForCausalLM`: Main model class (inherits PreTrainedModel + GenerationMixin)
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- Components: RMSNorm, RoPE + YaRN extrapolation, GQA (8 query heads, 4 KV heads), SwiGLU FFN, MoE with top-K routing and aux loss
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- Weight tying between input embedding and LM head (`tie_word_embeddings`)
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- Custom `generate()` with top-k/top-p sampling, repetition penalty, KV-cache
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- Model sizes controlled by `hidden_size`: 512 (minimind-3, 4 layers), 640 (minimind-2, 6 layers), 768 (default, 8 layers)
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### LoRA (`model/model_lora.py`)
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Pure PyTorch LoRA implementation (rank-16 A*B matrices). Functions: `apply_lora()`, `load_lora()`, `save_lora()`, `merge_lora()`.
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### Datasets (`dataset/lm_dataset.py`)
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- `PretrainDataset`: plain text JSONL
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- `SFTDataset`: multi-turn chat conversations
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- `DPODataset`: chosen/rejected pairs
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- `RLAIFDataset`: prompts for RL training
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- `AgentRLDataset`: multi-turn with tool calls
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All data is JSONL format in `dataset/`.
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### Shared Utilities (`trainer/trainer_utils.py`)
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- `init_model()`: model + tokenizer initialization, handles DDP unwrapping and torch.compile
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- `lm_checkpoint()`: atomic checkpoint save/load with `os.replace()`, handles multi-GPU world_size changes on resume
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- `init_distributed_mode()`: DDP setup
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- `setup_seed()`: deterministic seeding
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- `LMForRewardModel`: wrapper for reward models (uses InternLM2-1.8B-Reward)
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### Logging
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SwanLab (default) or WandB (`--use_wandb`). Checkpoints store wandb_id for resume.
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### Tokenizer (`model/`)
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Custom BPE tokenizer with 6,400 vocab, 36 special tokens. Chat template supports tools, multi-turn, and reasoning_content. `train_tokenizer.py` is reference-only (pre-trained tokenizer is used).
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## Key Conventions
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- No test framework exists; `scripts/eval_toolcall.py` provides tool call evaluation
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- All training scripts use argparse with defaults matching the minimind-768 config
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- Checkpoints are saved to `../checkpoints/` relative to `trainer/`
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- Code comments are in Chinese
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@ -4,8 +4,10 @@ import json
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import os
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import random
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from datasets import load_dataset, Features, Sequence, Value
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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def pre_processing_chat(conversations, add_system_ratio=0.2):
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# tool use 数据完整保留不做处理
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if any(conv.get('tools') for conv in conversations): return conversations
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@ -28,18 +30,20 @@ def pre_processing_chat(conversations, add_system_ratio=0.2):
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return [{'role': 'system', 'content': random.choice(SYSTEM_PROMPTS)}] + conversations
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return conversations
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def post_processing_chat(prompt_content, empty_think_ratio=0.2):
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# 以80%概率移除空思考标签
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if '<think>\n\n</think>\n\n' in prompt_content and random.random() > empty_think_ratio:
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prompt_content = prompt_content.replace('<think>\n\n</think>\n\n', '')
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return prompt_content
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class PretrainDataset(Dataset):
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def __init__(self, data_path, tokenizer, max_length=512):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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#load_dataset是HuggingFace datasets库上用来加载数据集的函数，可以加载HuggingFace Hub上的数据，也可以加载本地的json/csv/txt等文件
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# load_dataset是HuggingFace datasets库上用来加载数据集的函数，可以加载HuggingFace Hub上的数据，也可以加载本地的json/csv/txt等文件
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self.samples = load_dataset('json', data_files=data_path, split='train')
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def __len__(self):
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@ -47,7 +51,8 @@ class PretrainDataset(Dataset):
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def __getitem__(self, index):
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sample = self.samples[index]
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tokens = self.tokenizer(str(sample['text']), add_special_tokens=False, max_length=self.max_length - 2, truncation=True).input_ids
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tokens = self.tokenizer(str(sample['text']), add_special_tokens=False, max_length=self.max_length - 2,
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truncation=True).input_ids
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tokens = [self.tokenizer.bos_token_id] + tokens + [self.tokenizer.eos_token_id]
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input_ids = tokens + [self.tokenizer.pad_token_id] * (self.max_length - len(tokens))
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input_ids = torch.tensor(input_ids, dtype=torch.long)
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@ -61,7 +66,9 @@ class SFTDataset(Dataset):
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super().__init__()
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self.tokenizer = tokenizer
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self.max_length = max_length
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features = Features({'conversations': [{'role': Value('string'), 'content': Value('string'), 'reasoning_content': Value('string'), 'tools': Value('string'), 'tool_calls': Value('string')}]})
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features = Features({'conversations': [
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{'role': Value('string'), 'content': Value('string'), 'reasoning_content': Value('string'),
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'tools': Value('string'), 'tool_calls': Value('string')}]})
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self.samples = load_dataset('json', data_files=jsonl_path, split='train', features=features)
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self.bos_id = tokenizer(f'{tokenizer.bos_token}assistant\n', add_special_tokens=False).input_ids
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self.eos_id = tokenizer(f'{tokenizer.eos_token}\n', add_special_tokens=False).input_ids
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@ -74,15 +81,21 @@ class SFTDataset(Dataset):
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tools = None
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for message in conversations:
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message = dict(message)
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# 如果消息是system角色，且带了tools字段
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if message.get("role") == "system" and message.get("tools"):
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# json.loads把json格式字符串解析为python对象
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tools = json.loads(message["tools"]) if isinstance(message["tools"], str) else message["tools"]
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if message.get("tool_calls") and isinstance(message["tool_calls"], str):
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message["tool_calls"] = json.loads(message["tool_calls"])
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messages.append(message)
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return self.tokenizer.apply_chat_template(
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# messages是多轮对话列表
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messages,
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# 返回文本字符串，不做分词，如果是true则返回文本token id 列表
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tokenize=False,
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add_generation_prompt=False,
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# 传入工具定义，模版会在开头插入工具描述
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tools=tools
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)
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@ -215,6 +228,7 @@ class RLAIFDataset(Dataset):
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open_thinking=use_thinking,
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add_generation_prompt=True
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)
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def __getitem__(self, index):
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sample = self.samples[index]
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prompt = self.create_chat_prompt(sample['conversations'])
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@ -224,6 +238,7 @@ class RLAIFDataset(Dataset):
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'answer': ""
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}
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class AgentRLDataset(Dataset):
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def __init__(self, jsonl_path, tokenizer, max_length=1024):
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super().__init__()
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@ -14,9 +14,9 @@ from torch import optim, nn
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from torch.nn.parallel import DistributedDataParallel
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from torch.utils.data import DataLoader, DistributedSampler
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from model.model_minimind import MiniMindConfig
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from model.model_minimind_mla import MiniMindMLAConfig
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from dataset.lm_dataset import SFTDataset
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from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, init_model, SkipBatchSampler, get_model_suffix
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from trainer.trainer_utils import get_lr, Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, \
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init_model, SkipBatchSampler
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warnings.filterwarnings('ignore')
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@ -55,13 +55,15 @@ def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
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current_logits_loss = current_loss - current_aux_loss
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current_lr = optimizer.param_groups[-1]['lr']
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eta_min = spend_time / max(step - start_step, 1) * (iters - step) // 60
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Logger(f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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if wandb: wandb.log({"loss": current_loss, "logits_loss": current_logits_loss, "aux_loss": current_aux_loss, "learning_rate": current_lr, "epoch_time": eta_min})
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Logger(
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f'Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), loss: {current_loss:.4f}, logits_loss: {current_logits_loss:.4f}, aux_loss: {current_aux_loss:.4f}, lr: {current_lr:.8f}, epoch_time: {eta_min:.1f}min')
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if wandb: wandb.log({"loss": current_loss, "logits_loss": current_logits_loss, "aux_loss": current_aux_loss,
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"learning_rate": current_lr, "epoch_time": eta_min})
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if (step % args.save_interval == 0 or step == iters) and is_main_process():
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model.eval()
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model_suffix = get_model_suffix(lm_config)
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{model_suffix}.pth'
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moe_suffix = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
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raw_model = model.module if isinstance(model, DistributedDataParallel) else model
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raw_model = getattr(raw_model, '_orig_mod', raw_model)
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state_dict = raw_model.state_dict()
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@ -86,7 +88,7 @@ if __name__ == "__main__":
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parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
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parser.add_argument('--save_weight', default='full_sft', type=str, help="保存权重的前缀名")
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parser.add_argument("--epochs", type=int, default=2, help="训练轮数")
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parser.add_argument("--batch_size", type=int, default=16, help="batch size")
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parser.add_argument("--batch_size", type=int, default=48, help="batch size")
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parser.add_argument("--learning_rate", type=float, default=1e-5, help="初始学习率")
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
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@ -99,14 +101,14 @@ if __name__ == "__main__":
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parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
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parser.add_argument('--max_seq_len', default=768, type=int, help="训练的最大截断长度（中文1token≈1.5~1.7字符）")
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parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
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parser.add_argument('--use_mla', default=0, type=int, choices=[0, 1], help="是否使用MLA注意力架构（0=否，1=是）")
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parser.add_argument('--kv_lora_rank', default=128, type=int, help="MLA的KV压缩秩（仅use_mla=1时生效）")
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parser.add_argument("--data_path", type=str, default="../dataset/sft_t2t_mini.jsonl", help="训练数据路径")
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parser.add_argument('--from_weight', default='pretrain', type=str, help="基于哪个权重训练，为none则不基于任何权重训练")
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parser.add_argument('--from_weight', default='pretrain', type=str,
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help="基于哪个权重训练，为none则不基于任何权重训练")
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parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训（0=否，1=是）")
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parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT", help="wandb项目名")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1], help="是否使用torch.compile加速（0=否，1=是）")
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parser.add_argument("--use_compile", default=0, type=int, choices=[0, 1],
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help="是否使用torch.compile加速（0=否，1=是）")
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args = parser.parse_args()
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# ========== 1. 初始化环境和随机种子 ==========
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@ -116,11 +118,10 @@ if __name__ == "__main__":
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# ========== 2. 配置目录、模型参数、检查ckp ==========
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os.makedirs(args.save_dir, exist_ok=True)
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if args.use_mla:
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lm_config = MiniMindMLAConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe), kv_lora_rank=args.kv_lora_rank)
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else:
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=bool(args.use_moe))
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ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
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lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
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use_moe=bool(args.use_moe))
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ckp_data = lm_checkpoint(lm_config, weight=args.save_weight,
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save_dir='../checkpoints') if args.from_resume == 1 else None
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# ========== 3. 设置混合精度 ==========
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device_type = "cuda" if "cuda" in args.device else "cpu"
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@ -131,6 +132,7 @@ if __name__ == "__main__":
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wandb = None
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if args.use_wandb and is_main_process():
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import swanlab as wandb
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wandb_id = ckp_data.get('wandb_id') if ckp_data else None
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resume = 'must' if wandb_id else None
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wandb_run_name = f"MiniMind-Full-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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@ -162,7 +164,8 @@ if __name__ == "__main__":
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# ========== 8. 开始训练 ==========
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for epoch in range(start_epoch, args.epochs):
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train_sampler and train_sampler.set_epoch(epoch)
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setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
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setup_seed(42 + epoch);
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indices = torch.randperm(len(train_ds)).tolist()
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skip = start_step if (epoch == start_epoch and start_step > 0) else 0
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batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
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loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
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