diff --git a/README.md b/README.md index 97c0bf1..21f949b 100644 --- a/README.md +++ b/README.md @@ -93,9 +93,9 @@ | 模型 (大小) | 推理占用 (约) | Release | |-------------------------|----------|------------| -| MiniMind2-small (26M) | 0.5 GB | 2025.02.06 | -| MiniMind2-MoE (145M) | 1.0 GB | 2025.02.06 | -| MiniMind2 (104M) | 1.0 GB | 2025.02.06 | +| MiniMind2-small (26M) | 0.5 GB | 2025.04.26 | +| MiniMind2-MoE (145M) | 1.0 GB | 2025.04.26 | +| MiniMind2 (104M) | 1.0 GB | 2025.04.26 | | minimind-v1-small (26M) | 0.5 GB | 2024.08.28 | | minimind-v1-moe (4×26M) | 1.0 GB | 2024.09.17 | | minimind-v1 (108M) | 1.0 GB | 2024.09.01 | @@ -114,6 +114,7 @@ - 在第三方测评榜（C-Eval、C-MMLU、OpenBookQA等）进行模型测试。 - 实现Openai-Api协议的极简服务端，便于集成到第三方ChatUI使用（FastGPT、Open-WebUI等）。 - 基于streamlit实现最简聊天WebUI前端。 +- 全面兼容社区热门`llama.cpp`、`vllm`、`ollama`推理引擎或`Llama-Factory`训练框架。 - 复现(蒸馏/RL)大型推理模型DeepSeek-R1的MiniMind-Reason模型，**数据+模型**全部开源！ 希望此开源项目可以帮助LLM初学者快速入门！ @@ -121,7 +122,27 @@ ### 👉**更新日志**
- 2025-02-09 (newest 🎉🎉🎉) + 2025-04-26 (newest 🎉🎉🎉) + +- 重要更新 +- 如有兼容性需要，可访问[🔗旧仓库内容🔗](https://github.com/jingyaogong/minimind/tree/7da201a944a90ed49daef8a0265c959288dff83a)。 +- MiniMind模型参数完全改名，对齐Transformers库模型（统一命名）。 +- generate方式重构，继承自GenerationMixin类。 +- 🔥支持llama.cpp、vllm、ollama等热门三方生态。 +- 规范代码和目录结构。 +- 🔥更新：从0实现PPO、GRPO的训练代码。 +- 改动词表``->`<|im_start|><|im_end|>` +```text +为兼容第三方推理框架llama.cpp、vllm，本次更新需付出一些可观代价。 +本次更新不再支持「直接」加载25-04-26以前的旧模型进行推理。 +由于Llama位置编码方式与minimind存在区别，导致映射Llama模型后QK值存在差异 +MiniMind2系列旧模型均经过权重映射+（微调训练）QKVO线性层校准恢复而来。 +本次更新后将放弃对`minimind-v1`全系列的维护，并在仓库中下线。 +``` +
+ +
+ 2025-02-09 - 迎来发布以来重大更新，Release MiniMind2 Series。 - 代码几乎全部重构，使用更简洁明了的统一结构。 @@ -216,19 +237,19 @@ pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` ### 2.下载模型 - +到项目根目录 ```bash git clone https://huggingface.co/jingyaogong/MiniMind2 ``` -### 3.命令行问答 +### （可选）命令行问答 ```bash # load=0: load from pytorch model, load=1: load from transformers-hf model python eval_model.py --load 1 --model_mode 2 ``` -### 4.或启动WebUI +### （可选）启动WebUI ```bash # 可能需要`python>=3.10` 安装 `pip install streamlit` @@ -236,6 +257,15 @@ python eval_model.py --load 1 --model_mode 2 streamlit run web_demo.py ``` +### （可选）第三方推理框架 + +```bash +# ollama +ollama run jingyaogong/minimind2 +# vllm +vllm serve ./MiniMind2/ --served-model-name "minimind" +``` + ## Ⅱ 从0开始自己训练 ### 1.环境准备 @@ -273,6 +303,8 @@ print(torch.cuda.is_available()) ### 3.开始训练 +目录位于`trainer` + **3.1 预训练（学知识）** ```bash @@ -640,6 +672,8 @@ Zero模型权重保存为 `full_sft_512_zero.pth`（见下文MiniMind模型文 ## Ⅱ 主要训练步骤 +> 所有训练脚本均 `cd ./trainer` 目录执行 + ### **1. 预训练(Pretrain)**: LLM首先要学习的并非直接与人交流，而是让网络参数中充满知识的墨水，“墨水” 理论上喝的越饱越好，产生大量的对世界的知识积累。 @@ -677,6 +711,8 @@ python train_full_sft.py ## Ⅲ 其它训练步骤 +> 所有训练脚本均 `cd ./trainer` 目录执行 + ### **3. 人类反馈强化学习(Reinforcement Learning from Human Feedback, RLHF)** 在前面的训练步骤中，模型已经具备了基本的对话能力，但是这样的能力完全基于单词接龙，缺少正反样例的激励。 @@ -1214,16 +1250,13 @@ MiniMind模型本身预训练数据集小的可怜，也没有针对性的对测 # 📌 其它 (Others) -### 推理与导出 +## 模型转换 -* [./scripts/convert_model.py](./scripts/convert_model.py)可以将torch/transformers模型互相转换。 - -* MiniMind的HuggingFace集合地址： - [MiniMind](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5) +* [./scripts/convert_model.py](./scripts/convert_model.py)可以实现`torch模型/transformers`模型之间的转换 --- -### 基于MiniMind-API服务接口 +## 基于MiniMind-API服务接口 * [./scripts/serve_openai_api.py](./scripts/serve_openai_api.py)完成了兼容openai-api的最简聊天接口，方便将自己的模型接入第三方UI 例如FastGPT、OpenWebUI、Dify等等。 @@ -1265,6 +1298,74 @@ MiniMind模型本身预训练数据集小的可怜，也没有针对性的对测 }' ``` +## VLLM模型推理（服务） + +[vLLM](https://github.com/vllm-project/vllm)是极其流行的高效推理框架，支持大模型快速部署，优化显存利用与吞吐量。 + +```bash +vllm serve ./MiniMind2/ --model-impl transformers --served-model-name "minimind" +``` + +服务将以openai api协议启动，端口默认为8000。 + +更多用法请参考官方说明～ + +## llama.cpp +[llama.cpp](https://github.com/ggerganov/llama.cpp)是一个C++库， +可以在命令行下直接使用，支持多线程推理，支持GPU加速。 + +参考官方仓库安装后，在`convert_hf_to_gguf.py` ～760行插入 +```text +# 添加MiniMind2 tokenizer支持 +if res is None: + res = "smollm" +``` + +转换自定义训练的minimind模型 -> gguf +```bash +python convert_hf_to_gguf.py ../minimind/MiniMind2/ +``` + +量化模型 +```bash +./build/bin/llama-quantize ../minimind/MiniMind2/MiniMind2-109M-F16.gguf ../minimind/MiniMind2/Q4-MiniMind2.gguf Q4_K_M +``` + +命令行推理 +```bash +./build/bin/llama-cli -m ../minimind/MiniMind2/MiniMind2-109M-F16.gguf --chat-template chatml +``` + +更多用法请参考官方说明～ + +## ollama + +[ollama](https://ollama.ai/)是本地运行大模型的工具，支持多种开源LLM，简单易用。 + +通过ollama加载自定义的gguf模型，新建minimind.modelfile： +```text +FROM ./MiniMind2-109M-F16.gguf +TEMPLATE """{{ if .System }}<|im_start|>system +{{ .System }}<|im_end|> +{{ end }}{{ if .Prompt }}<|im_start|>user +{{ .Prompt }}<|im_end|> +{{ end }}<|im_start|>assistant +""" +``` + +加载模型并命名为`minimind2` +```bash +ollama create -f minimind.modelfile minimind2 +``` + +启动推理 +```text +ollama run minimind2 +> 你好，我是MiniMind2，一个基于xxxxxxxx +``` + +更多用法请参考官方说明～ + # 📌 Acknowledge > [!NOTE] diff --git a/README_en.md b/README_en.md index 5bb4db4..1412244 100644 --- a/README_en.md +++ b/README_en.md @@ -100,9 +100,9 @@ the entire process of building a language model from 0 to 1. Let's enjoy the fun | Model (Size) | Inference Usage (Approx.) | Release | |-------------------------|---------------------------|------------| -| MiniMind2-small (26M) | 0.5 GB | 2025.02.06 | -| MiniMind2-MoE (145M) | 1.0 GB | 2025.02.06 | -| MiniMind2 (104M) | 1.0 GB | 2025.02.06 | +| MiniMind2-small (26M) | 0.5 GB | 2025.04.26 | +| MiniMind2-MoE (145M) | 1.0 GB | 2025.04.26 | +| MiniMind2 (104M) | 1.0 GB | 2025.04.26 | | minimind-v1-small (26M) | 0.5 GB | 2024.08.28 | | minimind-v1-moe (4×26M) | 1.0 GB | 2024.09.17 | | minimind-v1 (108M) | 1.0 GB | 2024.09.01 | @@ -123,6 +123,7 @@ the entire process of building a language model from 0 to 1. Let's enjoy the fun - Model testing on third-party evaluation benchmarks (C-Eval, C-MMLU, OpenBookQA, etc.). - A minimal server implementing the Openai-Api protocol, easy to integrate into third-party ChatUI applications ( FastGPT, Open-WebUI, etc.). +- Fully compatible with popular community inference engines like llama.cpp, vllm, ollama, or training frameworks such as Llama-Factory. - A simple chat WebUI front-end implemented using streamlit. - Reproduction (distillation/RL) of the large inference model DeepSeek-R1 as the MiniMind-Reason model, **data + model** all open-source! @@ -131,8 +132,38 @@ We hope this open-source project can help LLM beginners quickly get started! ### 👉**Update log** +
+ 2025-04-26 (newest 🎉🎉🎉) + +• Major Updates + +• For compatibility needs, visit [🔗Legacy Repository Content🔗](https://github.com/jingyaogong/minimind/tree/7da201a944a90ed49daef8a0265c959288dff83a). + +• MiniMind model parameters have been fully renamed to align with Transformers library models (unified naming). + +• The `generate` method has been refactored, now inheriting from the `GenerationMixin` class. + +• 🔥 Support for popular third-party ecosystems like llama.cpp, vllm, and ollama. + +• Standardized code and directory structure. + +• 🔥 New: Added training code for PPO and GRPO from scratch. + +• Updated vocabulary tokens: `` → `<|im_start|><|im_end|>`. + + +```text +To ensure compatibility with third-party inference frameworks (llama.cpp, vllm), this update comes at a non-trivial cost. +Models saved before 2025-04-26 can no longer be **directly** loaded for inference. +Due to differences in positional encoding between Llama and MiniMind, QK values diverge after weight mapping. +MiniMind2 legacy models have been restored via weight mapping + (fine-tuning) QKVO linear layer calibration. +After this update, maintenance for the entire `minimind-v1` series will be discontinued, and the models will be removed from the repository. +``` +
+ +
- 2025-02-09 (newest 🎉🎉🎉) + 2025-02-09 - Major update since the release, with the release of MiniMind2 Series. - Almost all code has been refactored, using a more streamlined and unified structure. @@ -235,21 +266,30 @@ pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple git clone https://huggingface.co/jingyaogong/MiniMind2 ``` -### 3. Command-line Q&A +### (Optional) Command-line Q&A ```bash # load=0: load from pytorch model, load=1: load from transformers-hf model python eval_model.py --load 1 --model_mode 2 ``` -### 4. Or Start WebUI +### (Optional) Launch WebUI ```bash -# You may need `python>=3.10` and install `pip install streamlit`. +# May require `python>=3.10`, install with `pip install streamlit` # cd scripts streamlit run web_demo.py ``` +### (Optional) Third-party inference frameworks + +```bash +# ollama +ollama run jingyaogong/minimind2 +# vllm +vllm serve ./MiniMind2/ --served-model-name "minimind" +``` + ## Ⅱ Training from Scratch ### 1. Environment Setup @@ -292,6 +332,8 @@ needs and GPU resources. ### 3. Start Training +The directory is located at `trainer` + **3.1 Pretraining (Learning Knowledge)** ```bash @@ -696,6 +738,8 @@ download and test the model's performance. ## Ⅱ Main Training Steps +> All training scripts are executed in the `cd ./trainer` directory. + ### **1. Pretraining**: The first task for LLM is not to interact directly with humans, but to fill the network parameters with knowledge. The " @@ -744,6 +788,8 @@ python train_full_sft.py ## Ⅲ Other Training Steps +> All training scripts are executed in the `cd ./trainer` directory. + ### **3. Reinforcement Learning from Human Feedback (RLHF)** In the previous training steps, the model has acquired basic conversational abilities, but these are entirely based on @@ -1361,16 +1407,13 @@ is mainly for fun, so take the results lightly: # 📌 Others -### Inference and Export +## Model Conversion -* [./scripts/convert_model.py](./scripts/convert_model.py) can convert models between torch/transformers. - -* MiniMind's HuggingFace collection link: - [MiniMind](https://huggingface.co/collections/jingyaogong/minimind-66caf8d999f5c7fa64f399e5) +* [./scripts/convert_model.py](./scripts/convert_model.py) can be used to convert between `torch models` and `transformers` models. --- -### Based on MiniMind-API Service Interface +## Based on MiniMind-API Service Interface * [./scripts/serve_openai_api.py](./scripts/serve_openai_api.py) provides the simplest chat interface compatible with the OpenAI API, @@ -1415,6 +1458,73 @@ is mainly for fun, so take the results lightly: }' ``` +## VLLM Model Inference (Service) + +[vLLM](https://github.com/vllm-project/vllm) is an extremely popular and efficient inference framework that supports fast deployment of large models, optimizing memory utilization and throughput. + +```bash +vllm serve ./MiniMind2/ --model-impl transformers --served-model-name "minimind" +``` + +The service will start using the OpenAI API protocol, with the default port being 8000. + +For more usage, please refer to the official documentation. + +## llama.cpp +[llama.cpp](https://github.com/ggerganov/llama.cpp) is a C++ library that can be used directly from the command line, supporting multi-threaded inference and GPU acceleration. + +After installation (refer to the official repository), insert the following code at line 760 of `convert_hf_to_gguf.py`: +```text +# Add MiniMind2 tokenizer support +if res is None: + res = "smollm" +``` + +Convert a custom-trained MiniMind model to gguf: +```bash +python convert_hf_to_gguf.py ../minimind/MiniMind2/ +``` + +Quantize the model: +```bash +./build/bin/llama-quantize ../minimind/MiniMind2/MiniMind2-109M-F16.gguf ../minimind/MiniMind2/Q4-MiniMind2.gguf Q4_K_M +``` + +Command line inference: +```bash +./build/bin/llama-cli -m ../minimind/MiniMind2/MiniMind2-109M-F16.gguf --chat-template chatml +``` + +For more usage, please refer to the official documentation. + +## ollama + +[ollama](https://ollama.ai/) is a tool for running large models locally, supporting multiple open-source LLMs, and is easy to use. + +To load a custom gguf model with ollama, create a new file `minimind.modelfile`: +```text +FROM ./MiniMind2-109M-F16.gguf +TEMPLATE """{{ if .System }}<|im_start|>system +{{ .System }}<|im_end|> +{{ end }}{{ if .Prompt }}<|im_start|>user +{{ .Prompt }}<|im_end|> +{{ end }}<|im_start|>assistant +""" +``` + +Load the model and name it `minimind2`: +```bash +ollama create -f minimind.modelfile minimind2 +``` + +Start inference: +```text +ollama run minimind2 +> Hello, I am MiniMind2, based on xxxxxxxx +``` + +For more usage, please refer to the official documentation. + # 📌 Acknowledge > [!NOTE] diff --git a/eval_model.py b/eval_model.py index a031b52..f2f47ec 100644 --- a/eval_model.py +++ b/eval_model.py @@ -1,37 +1,32 @@ import argparse import random -import time -import numpy as np -import torch import warnings -from transformers import AutoTokenizer, AutoModelForCausalLM -from model.model import MiniMindLM -from model.LMConfig import LMConfig +import numpy as np +from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer +from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from model.model_lora import * warnings.filterwarnings('ignore') def init_model(args): - tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') + tokenizer = AutoTokenizer.from_pretrained('./model/') if args.load == 0: moe_path = '_moe' if args.use_moe else '' modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason', 4: 'grpo'} - ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.dim}{moe_path}.pth' + ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth' - model = MiniMindLM(LMConfig( - dim=args.dim, - n_layers=args.n_layers, - max_seq_len=args.max_seq_len, + model = MiniMindForCausalLM(MiniMindConfig( + hidden_size=args.hidden_size, + num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe )) - state_dict = torch.load(ckp, map_location=args.device) - model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=True) + model.load_state_dict(torch.load(ckp, map_location=args.device), strict=True) if args.lora_name != 'None': apply_lora(model) - load_lora(model, f'./{args.out_dir}/lora/{args.lora_name}_{args.dim}.pth') + load_lora(model, f'./{args.out_dir}/lora/{args.lora_name}_{args.hidden_size}.pth') else: transformers_model_path = './MiniMind2' tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) @@ -108,19 +103,18 @@ def main(): parser.add_argument('--temperature', default=0.85, type=float) parser.add_argument('--top_p', default=0.85, type=float) parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str) - # 此处max_seq_len（最大允许输入长度）并不意味模型具有对应的长文本的性能，仅防止QA出现被截断的问题 - # MiniMind2-moe (145M)：(dim=640, n_layers=8, use_moe=True) - # MiniMind2-Small (26M)：(dim=512, n_layers=8) - # MiniMind2 (104M)：(dim=768, n_layers=16) - parser.add_argument('--dim', default=512, type=int) - parser.add_argument('--n_layers', default=8, type=int) + # 此处max_seq_len（最大输出长度）并不意味模型具有对应的长文本的性能，仅防止QA出现被截断的问题 + # MiniMind2-moe (145M)：(hidden_size=640, num_hidden_layers=8, use_moe=True) + # MiniMind2-Small (26M)：(hidden_size=512, num_hidden_layers=8) + # MiniMind2 (104M)：(hidden_size=768, num_hidden_layers=16) + parser.add_argument('--hidden_size', default=640, type=int) + parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=8192, type=int) - parser.add_argument('--use_moe', default=False, type=bool) + parser.add_argument('--use_moe', default=True, type=bool) # 携带历史对话上下文条数 # history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文 # 模型未经过外推微调时，在更长的上下文的chat_template时难免出现性能的明显退化，因此需要注意此处设置 parser.add_argument('--history_cnt', default=0, type=int) - parser.add_argument('--stream', default=True, type=bool) parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载") parser.add_argument('--model_mode', default=1, type=int, help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型") @@ -130,6 +124,8 @@ def main(): prompts = get_prompt_datas(args) test_mode = int(input('[0] 自动测试\n[1] 手动输入\n')) + streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) + messages = [] for idx, prompt in enumerate(prompts if test_mode == 0 else iter(lambda: input('👶: '), '')): setup_seed(random.randint(0, 2048)) @@ -143,38 +139,31 @@ def main(): messages, tokenize=False, add_generation_prompt=True - )[-args.max_seq_len - 1:] if args.model_mode != 0 else (tokenizer.bos_token + prompt) + ) if args.model_mode != 0 else (tokenizer.bos_token + prompt) - answer = new_prompt - with torch.no_grad(): - x = torch.tensor(tokenizer(new_prompt)['input_ids'], device=args.device).unsqueeze(0) - outputs = model.generate( - x, - eos_token_id=tokenizer.eos_token_id, - max_new_tokens=args.max_seq_len, - temperature=args.temperature, - top_p=args.top_p, - stream=args.stream, - pad_token_id=tokenizer.pad_token_id - ) + inputs = tokenizer( + new_prompt, + return_tensors="pt", + truncation=True + ).to(args.device) - print('🤖️: ', end='') - try: - if not args.stream: - print(tokenizer.decode(outputs.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True), end='') - else: - history_idx = 0 - for y in outputs: - answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True) - if (answer and answer[-1] == '�') or not answer: - continue - print(answer[history_idx:], end='', flush=True) - history_idx = len(answer) - except StopIteration: - print("No answer") - print('\n') + print('🤖️: ', end='') + generated_ids = model.generate( + inputs["input_ids"], + max_new_tokens=args.max_seq_len, + num_return_sequences=1, + do_sample=True, + attention_mask=inputs["attention_mask"], + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + streamer=streamer, + top_p=args.top_p, + temperature=args.temperature + ) - messages.append({"role": "assistant", "content": answer}) + response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) + messages.append({"role": "assistant", "content": response}) + print('\n\n') if __name__ == "__main__": diff --git a/model/dataset.py b/lm_dataset.py similarity index 100% rename from model/dataset.py rename to lm_dataset.py diff --git a/model/LMConfig.py b/model/LMConfig.py deleted file mode 100644 index fb0a76d..0000000 --- a/model/LMConfig.py +++ /dev/null @@ -1,61 +0,0 @@ -from transformers import PretrainedConfig -from typing import List - - -class LMConfig(PretrainedConfig): - model_type = "minimind" - - def __init__( - self, - dim: int = 512, - n_layers: int = 8, - n_heads: int = 8, - n_kv_heads: int = 2, - vocab_size: int = 6400, - hidden_dim: int = None, - multiple_of: int = 64, - norm_eps: float = 1e-5, - max_seq_len: int = 8192, - rope_theta: int = 1e6, - dropout: float = 0.0, - flash_attn: bool = True, - #################################################### - # Here are the specific configurations of MOE - # When use_moe is false, the following is invalid - #################################################### - use_moe: bool = False, - #################################################### - num_experts_per_tok: int = 2, - n_routed_experts: int = 4, - n_shared_experts: bool = True, - scoring_func: str = 'softmax', - aux_loss_alpha: float = 0.1, - seq_aux: bool = True, - norm_topk_prob: bool = True, - **kwargs, - ): - self.dim = dim - self.n_layers = n_layers - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.vocab_size = vocab_size - self.hidden_dim = hidden_dim - self.multiple_of = multiple_of - self.norm_eps = norm_eps - self.max_seq_len = max_seq_len - self.rope_theta = rope_theta - self.dropout = dropout - self.flash_attn = flash_attn - #################################################### - # Here are the specific configurations of MOE - # When use_moe is false, the following is invalid - #################################################### - self.use_moe = use_moe - self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量 - self.n_routed_experts = n_routed_experts # 总的专家数量 - self.n_shared_experts = n_shared_experts # 共享专家 - self.scoring_func = scoring_func # 评分函数，默认为'softmax' - self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数 - self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失 - self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率 - super().__init__(**kwargs) diff --git a/model/__init__.py 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torch.Tensor: - """torch.repeat_interleave(x, dim=2, repeats=n_rep)""" - bs, slen, n_kv_heads, head_dim = x.shape - if n_rep == 1: - return x - return ( - x[:, :, :, None, :] - .expand(bs, slen, n_kv_heads, n_rep, head_dim) - .reshape(bs, slen, n_kv_heads * n_rep, head_dim) - ) - - -class Attention(nn.Module): - def __init__(self, args: LMConfig): - super().__init__() - self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads - assert args.n_heads % self.n_kv_heads == 0 - self.n_local_heads = args.n_heads - self.n_local_kv_heads = self.n_kv_heads - self.n_rep = self.n_local_heads // self.n_local_kv_heads - self.head_dim = args.dim // args.n_heads - self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) - self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) - self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) - self.attn_dropout = nn.Dropout(args.dropout) - self.resid_dropout = nn.Dropout(args.dropout) - self.dropout = args.dropout - self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn - # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") - mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) - mask = torch.triu(mask, diagonal=1) - self.register_buffer("mask", mask, persistent=False) - - def forward(self, - x: torch.Tensor, - pos_cis: torch.Tensor, - past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, - use_cache=False): - bsz, seq_len, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) - xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) - xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) - - xq, xk = apply_rotary_emb(xq, xk, pos_cis) - # kv_cache实现 - if past_key_value is not None: - xk = torch.cat([past_key_value[0], xk], dim=1) - xv = torch.cat([past_key_value[1], xv], dim=1) - past_kv = (xk, xv) if use_cache else None - - xq, xk, xv = ( - xq.transpose(1, 2), - repeat_kv(xk, self.n_rep).transpose(1, 2), - repeat_kv(xv, self.n_rep).transpose(1, 2) - ) - if self.flash and seq_len != 1: - dropout_p = self.dropout if self.training else 0.0 - output = F.scaled_dot_product_attention( - xq, xk, xv, - attn_mask=None, - dropout_p=dropout_p, - is_causal=True - ) - else: - scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) - scores += self.mask[:, :, :seq_len, :seq_len] - scores = F.softmax(scores.float(), dim=-1).type_as(xq) - scores = self.attn_dropout(scores) - output = scores @ xv - - output = output.transpose(1, 2).reshape(bsz, seq_len, -1) - output = self.resid_dropout(self.wo(output)) - return output, past_kv - - -class FeedForward(nn.Module): - def __init__(self, config: LMConfig): - super().__init__() - if config.hidden_dim is None: - hidden_dim = 4 * config.dim - hidden_dim = int(2 * hidden_dim / 3) - config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of) - self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False) - self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False) - self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False) - self.dropout = nn.Dropout(config.dropout) - - def forward(self, x): - return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) - - -class MoEGate(nn.Module): - def __init__(self, config: LMConfig): - super().__init__() - self.config = config - self.top_k = config.num_experts_per_tok - self.n_routed_experts = config.n_routed_experts - - self.scoring_func = config.scoring_func - self.alpha = config.aux_loss_alpha - self.seq_aux = config.seq_aux - - self.norm_topk_prob = config.norm_topk_prob - self.gating_dim = config.dim - self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) - self.reset_parameters() - - def reset_parameters(self) -> None: - import torch.nn.init as init - init.kaiming_uniform_(self.weight, a=math.sqrt(5)) - - def forward(self, hidden_states): - bsz, seq_len, h = hidden_states.shape - hidden_states = hidden_states.view(-1, h) - logits = F.linear(hidden_states, self.weight, None) - if self.scoring_func == 'softmax': - scores = logits.softmax(dim=-1) - else: - raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') - - topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) - - if self.top_k > 1 and self.norm_topk_prob: - denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 - topk_weight = topk_weight / denominator - - if self.training and self.alpha > 0.0: - scores_for_aux = scores - aux_topk = self.top_k - topk_idx_for_aux_loss = topk_idx.view(bsz, -1) - if self.seq_aux: - scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) - ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) - ce.scatter_add_(1, topk_idx_for_aux_loss, - torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( - seq_len * aux_topk / self.n_routed_experts) - aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha - else: - mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) - ce = mask_ce.float().mean(0) - Pi = scores_for_aux.mean(0) - fi = ce * self.n_routed_experts - aux_loss = (Pi * fi).sum() * self.alpha - else: - aux_loss = 0 - return topk_idx, topk_weight, aux_loss - - -class MOEFeedForward(nn.Module): - def __init__(self, config: LMConfig): - super().__init__() - self.config = config - self.experts = nn.ModuleList([ - FeedForward(config) - for _ in range(config.n_routed_experts) - ]) - self.gate = MoEGate(config) - if config.n_shared_experts is not None: - self.shared_experts = FeedForward(config) - - def forward(self, x): - identity = x - orig_shape = x.shape - bsz, seq_len, _ = x.shape - # 使用门控机制选择专家 - topk_idx, topk_weight, aux_loss = self.gate(x) - x = x.view(-1, x.shape[-1]) - flat_topk_idx = topk_idx.view(-1) - if self.training: - x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) - y = torch.empty_like(x, dtype=torch.float16) - for i, expert in enumerate(self.experts): - y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 - y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) - y = y.view(*orig_shape) - else: - y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) - if self.config.n_shared_experts is not None: - y = y + self.shared_experts(identity) - self.aux_loss = aux_loss - return y - - @torch.no_grad() - def moe_infer(self, x, flat_expert_indices, flat_expert_weights): - expert_cache = torch.zeros_like(x) - idxs = flat_expert_indices.argsort() - tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) - token_idxs = idxs // self.config.num_experts_per_tok - # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） - # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 - # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） - # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 - for i, end_idx in enumerate(tokens_per_expert): - start_idx = 0 if i == 0 else tokens_per_expert[i - 1] - if start_idx == end_idx: - continue - expert = self.experts[i] - exp_token_idx = token_idxs[start_idx:end_idx] - expert_tokens = x[exp_token_idx] - expert_out = expert(expert_tokens).to(expert_cache.dtype) - expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) - expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) - - return expert_cache - - -class MiniMindBlock(nn.Module): - def __init__(self, layer_id: int, config: LMConfig): - super().__init__() - self.n_heads = config.n_heads - self.dim = config.dim - self.head_dim = config.dim // config.n_heads - self.attention = Attention(config) - - self.layer_id = layer_id - self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps) - self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps) - self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config) - - def forward(self, x, pos_cis, past_key_value=None, use_cache=False): - h_attn, past_kv = self.attention( - self.attention_norm(x), - pos_cis, - past_key_value=past_key_value, - use_cache=use_cache - ) - h = x + h_attn - out = h + self.feed_forward(self.ffn_norm(h)) - return out, past_kv - - -class MiniMindLM(PreTrainedModel): - config_class = LMConfig - - def __init__(self, params: LMConfig = None): - self.params = params or LMConfig() - super().__init__(self.params) - self.vocab_size, self.n_layers = params.vocab_size, params.n_layers - self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) - self.dropout = nn.Dropout(params.dropout) - self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)]) - self.norm = RMSNorm(params.dim, eps=params.norm_eps) - self.output = nn.Linear(params.dim, params.vocab_size, bias=False) - self.tok_embeddings.weight = self.output.weight - self.register_buffer("pos_cis", - precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta), - persistent=False) - self.OUT = CausalLMOutputWithPast() - - def forward(self, - input_ids: Optional[torch.Tensor] = None, - past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, - use_cache: bool = False, - logits_to_keep: Union[int, torch.Tensor] = 0, - **args): - past_key_values = past_key_values or [None] * len(self.layers) - start_pos = args.get('start_pos', 0) - h = self.dropout(self.tok_embeddings(input_ids)) - pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)] - past_kvs = [] - for l, layer in enumerate(self.layers): - h, past_kv = layer( - h, pos_cis, - past_key_value=past_key_values[l], - use_cache=use_cache - ) - past_kvs.append(past_kv) - - slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep - logits = self.output(self.norm(h)[:, slice_indices, :]) - aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward)) - self.OUT.__setitem__('last_hidden_state', h) - self.OUT.__setitem__('logits', logits) - self.OUT.__setitem__('aux_loss', aux_loss) - self.OUT.__setitem__('past_key_values', past_kvs) - return self.OUT - - @torch.inference_mode() - def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90, - stream=False, rp=1., use_cache=True, pad_token_id=0, num_return_sequences=1, **args): - # 流式生成 - if stream: - return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args) - - # 直接生成 - generated = [] - for i in range(input_ids.size(0)): - non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0) - for _ in range(num_return_sequences): - out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args) - tokens_list = [tokens[:, -1:] for tokens in out] - gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad - full_sequence = torch.cat([non_pad, gen], dim=-1) - generated.append(full_sequence) - - max_length = max(seq.size(1) for seq in generated) - generated = [ - torch.cat( - [seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)], - dim=-1) - for seq in generated - ] - output = torch.cat(generated, dim=0) - res = output.view(input_ids.size(0) * num_return_sequences, -1) - return res - - def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args): - start, first_seq, past_kvs = input_ids.shape[1], True, None - while input_ids.shape[1] < max_new_tokens - 1: - if first_seq or not use_cache: - out, first_seq = self(input_ids, past_key_values=past_kvs, use_cache=use_cache, **args), False - else: - out = self(input_ids[:, -1:], past_key_values=past_kvs, use_cache=use_cache, - start_pos=input_ids.shape[1] - 1, **args) - logits, past_kvs = out.logits[:, -1, :], out.past_key_values - logits[:, list(set(input_ids.tolist()[0]))] /= rp - logits /= (temperature + 1e-9) - if top_p is not None and top_p < 1.0: - sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1) - sorted_probs = F.softmax(sorted_logits, dim=-1) - cumulative_probs = torch.cumsum(sorted_probs, dim=-1) - sorted_indices_to_remove = cumulative_probs > top_p - sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone() - sorted_indices_to_remove[:, 0] = False - indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove) - logits[indices_to_remove] = -float('Inf') - input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1) - input_ids = torch.cat((input_ids, input_ids_next), dim=1) - yield input_ids[:, start:] - if input_ids_next.item() == eos_token_id: - break diff --git a/model/model_minimind.py b/model/model_minimind.py new file mode 100644 index 0000000..92fe61b --- /dev/null +++ b/model/model_minimind.py @@ -0,0 +1,446 @@ +# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 +# MiniMind Config +# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 + +from transformers import PretrainedConfig + + +class MiniMindConfig(PretrainedConfig): + model_type = "minimind" + + def __init__( + self, + dropout: float = 0.0, + bos_token_id: int = 1, + eos_token_id: int = 2, + hidden_act: str = 'silu', + hidden_size: int = 512, + intermediate_size: int = None, + max_position_embeddings: int = 32768, + num_attention_heads: int = 8, + num_hidden_layers: int = 8, + num_key_value_heads: int = 2, + vocab_size: int = 6400, + rms_norm_eps: float = 1e-05, + rope_theta: int = 1000000.0, + flash_attn: bool = True, + #################################################### + # Here are the specific configurations of MOE + # When use_moe is false, the following is invalid + #################################################### + use_moe: bool = False, + num_experts_per_tok: int = 2, + n_routed_experts: int = 4, + n_shared_experts: int = 1, + scoring_func: str = 'softmax', + aux_loss_alpha: float = 0.1, + seq_aux: bool = True, + norm_topk_prob: bool = True, + **kwargs + ): + super().__init__(**kwargs) + self.dropout = dropout + self.bos_token_id = bos_token_id + self.eos_token_id = eos_token_id + self.hidden_act = hidden_act + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.max_position_embeddings = max_position_embeddings + self.num_attention_heads = num_attention_heads + self.num_hidden_layers = num_hidden_layers + self.num_key_value_heads = num_key_value_heads + self.vocab_size = vocab_size + self.rms_norm_eps = rms_norm_eps + self.rope_theta = rope_theta + self.flash_attn = flash_attn + #################################################### + # Here are the specific configurations of MOE + # When use_moe is false, the following is invalid + #################################################### + self.use_moe = use_moe + self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量 + self.n_routed_experts = n_routed_experts # 总的专家数量 + self.n_shared_experts = n_shared_experts # 共享专家 + self.scoring_func = scoring_func # 评分函数，默认为'softmax' + self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数 + self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失 + self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率 + + +# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 +# MiniMind Model +# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 + +import math +import torch +from torch import nn +from transformers.activations import ACT2FN +from typing import Optional, Tuple, List, Union +import torch.nn.functional as F +from transformers import PreTrainedModel, GenerationMixin, PretrainedConfig +from transformers.modeling_outputs import CausalLMOutputWithPast + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self.weight * self._norm(x.float()).type_as(x) + + +def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) + t = torch.arange(end, device=freqs.device) + freqs = torch.outer(t, freqs).float() + freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) + freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) + return freqs_cos, freqs_sin + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + def rotate_half(x): + return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1) + + q_embed = (q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim)) + k_embed = (k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim)) + return q_embed, k_embed + + +def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: + """torch.repeat_interleave(x, dim=2, repeats=n_rep)""" + bs, slen, num_key_value_heads, head_dim = x.shape + if n_rep == 1: + return x + return ( + x[:, :, :, None, :] + .expand(bs, slen, num_key_value_heads, n_rep, head_dim) + .reshape(bs, slen, num_key_value_heads * n_rep, head_dim) + ) + + +class Attention(nn.Module): + def __init__(self, args: MiniMindConfig): + super().__init__() + self.num_key_value_heads = args.num_attention_heads if args.num_key_value_heads is None else args.num_key_value_heads + assert args.num_attention_heads % self.num_key_value_heads == 0 + self.n_local_heads = args.num_attention_heads + self.n_local_kv_heads = self.num_key_value_heads + self.n_rep = self.n_local_heads // self.n_local_kv_heads + self.head_dim = args.hidden_size // args.num_attention_heads + self.q_proj = nn.Linear(args.hidden_size, args.num_attention_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(args.num_attention_heads * self.head_dim, args.hidden_size, bias=False) + self.attn_dropout = nn.Dropout(args.dropout) + self.resid_dropout = nn.Dropout(args.dropout) + self.dropout = args.dropout + self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn + # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") + + def forward(self, + x: torch.Tensor, + position_embeddings: Tuple[torch.Tensor, torch.Tensor], # 修改为接收cos和sin + past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + use_cache=False, + attention_mask: Optional[torch.Tensor] = None): + bsz, seq_len, _ = x.shape + xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x) + xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) + xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) + + cos, sin = position_embeddings + xq, xk = apply_rotary_pos_emb(xq, xk, cos[:seq_len], sin[:seq_len]) + + # kv_cache实现 + if past_key_value is not None: + xk = torch.cat([past_key_value[0], xk], dim=1) + xv = torch.cat([past_key_value[1], xv], dim=1) + past_kv = (xk, xv) if use_cache else None + + xq, xk, xv = ( + xq.transpose(1, 2), + repeat_kv(xk, self.n_rep).transpose(1, 2), + repeat_kv(xv, self.n_rep).transpose(1, 2) + ) + + if False and self.flash and seq_len != 1: + dropout_p = self.dropout if self.training else 0.0 + attn_mask = None + if attention_mask is not None: + attn_mask = attention_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_local_heads, seq_len, -1) + attn_mask = attn_mask.bool() if attention_mask is not None else None + + output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=True) + else: + scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) + scores = scores + torch.triu( + torch.full((seq_len, seq_len), float("-inf"), device=scores.device), + diagonal=1 + ).unsqueeze(0).unsqueeze(0) # scores+mask + + if attention_mask is not None: + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + extended_attention_mask = (1.0 - extended_attention_mask) * -1e9 + scores = scores + extended_attention_mask + + scores = F.softmax(scores.float(), dim=-1).type_as(xq) + scores = self.attn_dropout(scores) + output = scores @ xv + + output = output.transpose(1, 2).reshape(bsz, seq_len, -1) + output = self.resid_dropout(self.o_proj(output)) + return output, past_kv + + +class FeedForward(nn.Module): + def __init__(self, config: MiniMindConfig): + super().__init__() + if config.intermediate_size is None: + intermediate_size = int(config.hidden_size * 8 / 3) + config.intermediate_size = 64 * ((intermediate_size + 64 - 1) // 64) + self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.dropout = nn.Dropout(config.dropout) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))) + + +class MoEGate(nn.Module): + def __init__(self, config: MiniMindConfig): + super().__init__() + self.config = config + self.top_k = config.num_experts_per_tok + self.n_routed_experts = config.n_routed_experts + + self.scoring_func = config.scoring_func + self.alpha = config.aux_loss_alpha + self.seq_aux = config.seq_aux + + self.norm_topk_prob = config.norm_topk_prob + self.gating_dim = config.hidden_size + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) + self.reset_parameters() + + def reset_parameters(self) -> None: + import torch.nn.init as init + init.kaiming_uniform_(self.weight, a=math.sqrt(5)) + + def forward(self, hidden_states): + bsz, seq_len, h = hidden_states.shape + hidden_states = hidden_states.view(-1, h) + logits = F.linear(hidden_states, self.weight, None) + if self.scoring_func == 'softmax': + scores = logits.softmax(dim=-1) + else: + raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') + + topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) + + if self.top_k > 1 and self.norm_topk_prob: + denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 + topk_weight = topk_weight / denominator + + if self.training and self.alpha > 0.0: + scores_for_aux = scores + aux_topk = self.top_k + topk_idx_for_aux_loss = topk_idx.view(bsz, -1) + if self.seq_aux: + scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) + ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) + ce.scatter_add_(1, topk_idx_for_aux_loss, + torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( + seq_len * aux_topk / self.n_routed_experts) + aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha + else: + mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) + ce = mask_ce.float().mean(0) + Pi = scores_for_aux.mean(0) + fi = ce * self.n_routed_experts + aux_loss = (Pi * fi).sum() * self.alpha + else: + aux_loss = 0 + return topk_idx, topk_weight, aux_loss + + +class MOEFeedForward(nn.Module): + def __init__(self, config: MiniMindConfig): + super().__init__() + self.config = config + self.experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_routed_experts) + ]) + self.gate = MoEGate(config) + if config.n_shared_experts > 0: + self.shared_experts = nn.ModuleList([ + FeedForward(config) + for _ in range(config.n_shared_experts) + ]) + + def forward(self, x): + identity = x + orig_shape = x.shape + bsz, seq_len, _ = x.shape + # 使用门控机制选择专家 + topk_idx, topk_weight, aux_loss = self.gate(x) + x = x.view(-1, x.shape[-1]) + flat_topk_idx = topk_idx.view(-1) + if self.training: + x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) + y = torch.empty_like(x, dtype=torch.float16) + for i, expert in enumerate(self.experts): + y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 + y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) + y = y.view(*orig_shape) + else: + y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) + if self.config.n_shared_experts > 0: + for expert in self.shared_experts: + y = y + expert(identity) + self.aux_loss = aux_loss + return y + + @torch.no_grad() + def moe_infer(self, x, flat_expert_indices, flat_expert_weights): + expert_cache = torch.zeros_like(x) + idxs = flat_expert_indices.argsort() + tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) + token_idxs = idxs // self.config.num_experts_per_tok + # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） + # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 + # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） + # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 + for i, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if i == 0 else tokens_per_expert[i - 1] + if start_idx == end_idx: + continue + expert = self.experts[i] + exp_token_idx = token_idxs[start_idx:end_idx] + expert_tokens = x[exp_token_idx] + expert_out = expert(expert_tokens).to(expert_cache.dtype) + expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) + expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) + + return expert_cache + + +class MiniMindBlock(nn.Module): + def __init__(self, layer_id: int, config: MiniMindConfig): + super().__init__() + self.num_attention_heads = config.num_attention_heads + self.hidden_size = config.hidden_size + self.head_dim = config.hidden_size // config.num_attention_heads + self.self_attn = Attention(config) + + self.layer_id = layer_id + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config) + + def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None): + residual = hidden_states + hidden_states, present_key_value = self.self_attn( + self.input_layernorm(hidden_states), position_embeddings, + past_key_value, use_cache, attention_mask + ) + hidden_states += residual + hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states)) + return hidden_states, present_key_value + + +class MiniMindModel(nn.Module): + def __init__(self, config: MiniMindConfig): + super().__init__() + self.config = config + self.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) + self.dropout = nn.Dropout(config.dropout) + self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)]) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.hidden_size // config.num_attention_heads, + end=config.max_position_embeddings, theta=config.rope_theta) + self.register_buffer("freqs_cos", freqs_cos, persistent=False) + self.register_buffer("freqs_sin", freqs_sin, persistent=False) + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, + use_cache: bool = False, + **kwargs): + batch_size, seq_length = input_ids.shape + past_key_values = past_key_values or [None] * len(self.layers) + start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0 + + hidden_states = self.dropout(self.embed_tokens(input_ids)) + + position_embeddings = ( + self.freqs_cos[start_pos:start_pos + seq_length], + self.freqs_sin[start_pos:start_pos + seq_length] + ) + + presents = [] + for layer_idx, (layer, past_key_value) in enumerate(zip(self.layers, past_key_values)): + hidden_states, present = layer( + hidden_states, + position_embeddings, + past_key_value=past_key_value, + use_cache=use_cache, + attention_mask=attention_mask + ) + presents.append(present) + + hidden_states = self.norm(hidden_states) + + aux_loss = sum( + layer.mlp.aux_loss + for layer in self.layers + if isinstance(layer.mlp, MOEFeedForward) + ) + + return hidden_states, presents, aux_loss + + +class MiniMindForCausalLM(PreTrainedModel, GenerationMixin): + config_class = MiniMindConfig + + def __init__(self, config: MiniMindConfig = None): + self.config = config or MiniMindConfig() + super().__init__(self.config) + self.model = MiniMindModel(self.config) + self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False) + self.model.embed_tokens.weight = self.lm_head.weight + self.OUT = CausalLMOutputWithPast() + + def forward(self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, + use_cache: bool = False, + logits_to_keep: Union[int, torch.Tensor] = 0, + **args): + h, past_kvs, aux_loss = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **args + ) + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(h[:, slice_indices, :]) + self.OUT.__setitem__('last_hidden_state', h) + self.OUT.__setitem__('logits', logits) + self.OUT.__setitem__('aux_loss', aux_loss) + self.OUT.__setitem__('past_key_values', past_kvs) + return self.OUT diff --git a/scripts/chat_openai_api.py b/scripts/chat_openai_api.py index 2f2bc53..57f61c9 100644 --- a/scripts/chat_openai_api.py +++ b/scripts/chat_openai_api.py @@ -1,8 +1,8 @@ from openai import OpenAI client = OpenAI( - api_key="none", - base_url="http://localhost:8998/v1" + api_key="ollama", + base_url="http://127.0.0.1:8998/v1" ) stream = True conversation_history_origin = [] diff --git a/scripts/convert_model.py b/scripts/convert_model.py index 9c2209f..685cf9e 100644 --- a/scripts/convert_model.py +++ b/scripts/convert_model.py @@ -1,33 +1,57 @@ -import torch -import warnings -import sys import os +import sys __package__ = "scripts" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) -from transformers import AutoTokenizer, AutoModelForCausalLM -from model.LMConfig import LMConfig -from model.model import MiniMindLM +import torch +import warnings +from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM +from model.model_minimind import MiniMindConfig, MiniMindForCausalLM warnings.filterwarnings('ignore', category=UserWarning) -def convert_torch2transformers(torch_path, transformers_path): - def export_tokenizer(transformers_path): - tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer') - tokenizer.save_pretrained(transformers_path) - - LMConfig.register_for_auto_class() - MiniMindLM.register_for_auto_class("AutoModelForCausalLM") - lm_model = MiniMindLM(lm_config) +# MoE模型需使用此函数转换 +def convert_torch2transformers_minimind(torch_path, transformers_path, dtype=torch.bfloat16): + MiniMindConfig.register_for_auto_class() + MiniMindForCausalLM.register_for_auto_class("AutoModelForCausalLM") + lm_model = MiniMindForCausalLM(lm_config) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') state_dict = torch.load(torch_path, map_location=device) lm_model.load_state_dict(state_dict, strict=False) + lm_model = lm_model.to(dtype) # 转换模型权重精度 model_params = sum(p.numel() for p in lm_model.parameters() if p.requires_grad) print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)') lm_model.save_pretrained(transformers_path, safe_serialization=False) - export_tokenizer(transformers_path) - print(f"模型已保存为 Transformers 格式: {transformers_path}") + tokenizer = AutoTokenizer.from_pretrained('../model/') + tokenizer.save_pretrained(transformers_path) + print(f"模型已保存为 Transformers-MiniMind 格式: {transformers_path}") + + +# LlamaForCausalLM结构兼容第三方生态 +def convert_torch2transformers_llama(torch_path, transformers_path, dtype=torch.bfloat16): + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + state_dict = torch.load(torch_path, map_location=device) + llama_config = LlamaConfig( + vocab_size=lm_config.vocab_size, + hidden_size=lm_config.hidden_size, + intermediate_size=64 * ((int(lm_config.hidden_size * 8 / 3) + 64 - 1) // 64), + num_hidden_layers=lm_config.num_hidden_layers, + num_attention_heads=lm_config.num_attention_heads, + num_key_value_heads=lm_config.num_key_value_heads, + max_position_embeddings=lm_config.max_seq_len, + rms_norm_eps=lm_config.rms_norm_eps, + rope_theta=lm_config.rope_theta, + ) + llama_model = LlamaForCausalLM(llama_config) + llama_model.load_state_dict(state_dict, strict=False) + llama_model = llama_model.to(dtype) # 转换模型权重精度 + llama_model.save_pretrained(transformers_path) + model_params = sum(p.numel() for p in llama_model.parameters() if p.requires_grad) + print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)') + tokenizer = AutoTokenizer.from_pretrained('../model/') + tokenizer.save_pretrained(transformers_path) + print(f"模型已保存为 Transformers-Llama 格式: {transformers_path}") def convert_transformers2torch(transformers_path, torch_path): @@ -36,27 +60,15 @@ def convert_transformers2torch(transformers_path, torch_path): print(f"模型已保存为 PyTorch 格式: {torch_path}") -# don't need to use -def push_to_hf(export_model_path): - def init_model(): - tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer') - model = AutoModelForCausalLM.from_pretrained(export_model_path, trust_remote_code=True) - return model, tokenizer - - model, tokenizer = init_model() - # model.push_to_hub(model_path) - # tokenizer.push_to_hub(model_path, safe_serialization=False) - if __name__ == '__main__': - lm_config = LMConfig(dim=512, n_layers=8, max_seq_len=8192, use_moe=False) + lm_config = MiniMindConfig(hidden_size=768, num_hidden_layers=16, max_seq_len=8192, use_moe=True) - torch_path = f"../out/rlhf_{lm_config.dim}{'_moe' if lm_config.use_moe else ''}.pth" + torch_path = f"../out/full_sft_{lm_config.hidden_size}{'_moe' if lm_config.use_moe else ''}.pth" - transformers_path = '../MiniMind2-Small' + transformers_path = '../MiniMind2-MoE' - # convert torch to transformers model - convert_torch2transformers(torch_path, transformers_path) + convert_torch2transformers_minimind(torch_path, transformers_path) - # # convert transformers to torch model - # convert_transformers2torch(transformers_path, torch_path) + # # # convert transformers to torch model + # # convert_transformers2torch(transformers_path, torch_path) diff --git a/scripts/serve_openai_api.py b/scripts/serve_openai_api.py index 721d4e5..183efdb 100644 --- a/scripts/serve_openai_api.py +++ b/scripts/serve_openai_api.py @@ -9,12 +9,14 @@ import time import torch import warnings import uvicorn + +from threading import Thread +from queue import Queue from fastapi import FastAPI, HTTPException from fastapi.responses import StreamingResponse from pydantic import BaseModel -from transformers import AutoTokenizer, AutoModelForCausalLM -from model.LMConfig import LMConfig -from model.model import MiniMindLM +from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer +from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from model.model_lora import apply_lora, load_lora warnings.filterwarnings('ignore') @@ -23,30 +25,25 @@ app = FastAPI() def init_model(args): - tokenizer = AutoTokenizer.from_pretrained('../model/minimind_tokenizer') if args.load == 0: + tokenizer = AutoTokenizer.from_pretrained('../model/') moe_path = '_moe' if args.use_moe else '' modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason'} - ckp = f'../{args.out_dir}/{modes[args.model_mode]}_{args.dim}{moe_path}.pth' - - model = MiniMindLM(LMConfig( - dim=args.dim, - n_layers=args.n_layers, + ckp = f'../{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth' + model = MiniMindForCausalLM(MiniMindConfig( + hidden_size=args.hidden_size, + num_hidden_layers=args.num_hidden_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe )) - - state_dict = torch.load(ckp, map_location=device) - model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=True) - + model.load_state_dict(torch.load(ckp, map_location=device), strict=True) if args.lora_name != 'None': apply_lora(model) - load_lora(model, f'../{args.out_dir}/{args.lora_name}_{args.dim}.pth') + load_lora(model, f'../{args.out_dir}/{args.lora_name}_{args.hidden_size}.pth') else: - model = AutoModelForCausalLM.from_pretrained( - './MiniMind2', - trust_remote_code=True - ) + model_path = '../MiniMind2' + model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) + tokenizer = AutoTokenizer.from_pretrained(model_path) print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') return model.eval().to(device), tokenizer @@ -58,42 +55,61 @@ class ChatRequest(BaseModel): top_p: float = 0.92 max_tokens: int = 8192 stream: bool = False + tools: list = [] + + +class CustomStreamer(TextStreamer): + def __init__(self, tokenizer, queue): + super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True) + self.queue = queue + self.tokenizer = tokenizer + + def on_finalized_text(self, text: str, stream_end: bool = False): + self.queue.put(text) + if stream_end: + self.queue.put(None) def generate_stream_response(messages, temperature, top_p, max_tokens): try: new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)[-max_tokens:] - x = tokenizer(new_prompt).data['input_ids'] - x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...]) - with torch.no_grad(): - res_y = model.generate( - x, - eos_token_id=tokenizer.eos_token_id, + inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device) + + queue = Queue() + streamer = CustomStreamer(tokenizer, queue) + + def _generate(): + model.generate( + inputs.input_ids, max_new_tokens=max_tokens, + do_sample=True, temperature=temperature, top_p=top_p, - stream=True, - rp=1., - pad_token_id=tokenizer.pad_token_id + attention_mask=inputs.attention_mask, + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + streamer=streamer ) - history_idx = 0 - for y in res_y: - answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True) - if (answer and answer[-1] == '�') or not answer: - continue - delta = answer[history_idx:] - history_idx = len(answer) - json_data = { - 'id': f'chatcmpl-{int(time.time())}', - 'object': 'chat.completion.chunk', - 'created': int(time.time()), - 'model': 'minimind', - 'choices': [{'index': 0, 'delta': {'content': delta}, 'finish_reason': None}] - } - yield f"data: {json.dumps(json_data)}\n\n" + + Thread(target=_generate).start() + + while True: + text = queue.get() + if text is None: + yield json.dumps({ + "choices": [{ + "delta": {}, + "finish_reason": "stop" + }] + }, ensure_ascii=False) + break + + yield json.dumps({ + "choices": [{"delta": {"content": text}}] + }, ensure_ascii=False) except Exception as e: - yield f"data: {json.dumps({'error': str(e)})}\n\n" + yield json.dumps({"error": str(e)}) @app.post("/v1/chat/completions") @@ -101,12 +117,12 @@ async def chat_completions(request: ChatRequest): try: if request.stream: return StreamingResponse( - generate_stream_response( + (f"data: {chunk}\n\n" for chunk in generate_stream_response( messages=request.messages, temperature=request.temperature, top_p=request.top_p, max_tokens=request.max_tokens - ), + )), media_type="text/event-stream" ) else: @@ -115,20 +131,19 @@ async def chat_completions(request: ChatRequest): tokenize=False, add_generation_prompt=True )[-request.max_tokens:] - x = tokenizer(new_prompt).data['input_ids'] - x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...]) + inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device) with torch.no_grad(): - res_y = model.generate( - x, + generated_ids = model.generate( + inputs["input_ids"], + max_length=inputs["input_ids"].shape[1] + request.max_tokens, + do_sample=True, + attention_mask=inputs["attention_mask"], + pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, - max_new_tokens=request.max_tokens, - temperature=request.temperature, top_p=request.top_p, - stream=False, - rp=1., - pad_token_id=tokenizer.pad_token_id + temperature=request.temperature ) - answer = tokenizer.decode(res_y.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True) + answer = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) return { "id": f"chatcmpl-{int(time.time())}", "object": "chat.completion", @@ -142,7 +157,6 @@ async def chat_completions(request: ChatRequest): } ] } - except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @@ -151,14 +165,13 @@ if __name__ == "__main__": parser = argparse.ArgumentParser(description="Server for MiniMind") parser.add_argument('--out_dir', default='out', type=str) parser.add_argument('--lora_name', default='None', type=str) - parser.add_argument('--dim', default=512, type=int) - parser.add_argument('--n_layers', default=8, type=int) + parser.add_argument('--hidden_size', default=768, type=int) + parser.add_argument('--num_hidden_layers', default=16, type=int) parser.add_argument('--max_seq_len', default=8192, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument('--load', default=0, type=int, help="0: 从原生torch权重，1: 利用transformers加载") - parser.add_argument('--model_mode', default=1, type=int, help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型") - + parser.add_argument('--model_mode', default=1, type=int, + help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型") device = 'cuda' if torch.cuda.is_available() else 'cpu' model, tokenizer = init_model(parser.parse_args()) - uvicorn.run(app, host="0.0.0.0", port=8998) diff --git a/scripts/train_tokenizer.py b/scripts/train_tokenizer.py index 868099a..9cf5934 100644 --- a/scripts/train_tokenizer.py +++ b/scripts/train_tokenizer.py @@ -1,14 +1,9 @@ import random -from tqdm import tqdm -from transformers import AutoTokenizer import json -from datasets import load_dataset from tokenizers import ( decoders, models, - normalizers, pre_tokenizers, - processors, trainers, Tokenizer, ) @@ -32,7 +27,7 @@ def train_tokenizer(): tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) # 定义特殊token - special_tokens = ["", "", ""] + special_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"] # 设置训练器并添加特殊token trainer = trainers.BpeTrainer( @@ -52,15 +47,15 @@ def train_tokenizer(): tokenizer.decoder = decoders.ByteLevel() # 检查特殊token的索引 - assert tokenizer.token_to_id("") == 0 - assert tokenizer.token_to_id("") == 1 - assert tokenizer.token_to_id("") == 2 + assert tokenizer.token_to_id("<|endoftext|>") == 0 + assert tokenizer.token_to_id("<|im_start|>") == 1 + assert tokenizer.token_to_id("<|im_end|>") == 2 # 保存tokenizer - tokenizer_dir = "../model/minimind_tokenizer" + tokenizer_dir = "../model/" os.makedirs(tokenizer_dir, exist_ok=True) tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json")) - tokenizer.model.save("../model/minimind_tokenizer") + tokenizer.model.save("../model/") # 手动创建配置文件 config = { @@ -69,7 +64,7 @@ def train_tokenizer(): "add_prefix_space": False, "added_tokens_decoder": { "0": { - "content": "", + "content": "<|endoftext|>", "lstrip": False, "normalized": False, "rstrip": False, @@ -77,7 +72,7 @@ def train_tokenizer(): "special": True }, "1": { - "content": "", + "content": "<|im_start|>", "lstrip": False, "normalized": False, "rstrip": False, @@ -85,7 +80,7 @@ def train_tokenizer(): "special": True }, "2": { - "content": "", + "content": "<|im_end|>", "lstrip": False, "normalized": False, "rstrip": False, @@ -94,17 +89,17 @@ def train_tokenizer(): } }, "additional_special_tokens": [], - "bos_token": "", + "bos_token": "<|im_start|>", "clean_up_tokenization_spaces": False, - "eos_token": "", + "eos_token": "<|im_end|>", "legacy": True, "model_max_length": 32768, - "pad_token": "", + "pad_token": "<|endoftext|>", "sp_model_kwargs": {}, "spaces_between_special_tokens": False, "tokenizer_class": "PreTrainedTokenizerFast", - "unk_token": "", - "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ 'system\\n' + system_message + '\\n' }}{% else %}{{ 'system\\n你是 MiniMind，是一个有用的人工智能助手。\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ 'user\\n' + content + '\\nassistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '' + '\\n' }}{% endif %}{% endfor %}" + "unk_token": "<|endoftext|>", + "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ '<|im_start|>system\\n' + system_message + '<|im_end|>\\n' }}{% else %}{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}" } # 保存配置文件 @@ -118,7 +113,7 @@ def eval_tokenizer(): from transformers import AutoTokenizer # 加载预训练的tokenizer - tokenizer = AutoTokenizer.from_pretrained("../model/minimind_tokenizer") + tokenizer = AutoTokenizer.from_pretrained("../model/") messages = [ {"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"}, diff --git a/scripts/web_demo.py b/scripts/web_demo.py index be05159..6ed4b2f 100644 --- a/scripts/web_demo.py +++ b/scripts/web_demo.py @@ -1,14 +1,13 @@ import random import re -import time +from threading import Thread +import torch import numpy as np import streamlit as st -import torch st.set_page_config(page_title="MiniMind", initial_sidebar_state="collapsed") -# 在文件开头的 CSS 样式中修改按钮样式 st.markdown("""