From 9e35e5348fe5ea223d2859ce2343fb4f0b2fb077 Mon Sep 17 00:00:00 2001 From: math-zhuxy <235934429@qq.com> Date: Sun, 25 May 2025 16:14:33 +0800 Subject: [PATCH 1/4] new model_chat.py --- .gitignore | 3 +- model_chat.py | 100 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 102 insertions(+), 1 deletion(-) create mode 100644 model_chat.py diff --git a/.gitignore b/.gitignore index de6bd7a..ec6a5f5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ /model/__pycache__ /dataset -/out \ No newline at end of file +/out +/MiniMind2 \ No newline at end of file diff --git a/model_chat.py b/model_chat.py new file mode 100644 index 0000000..6a08f19 --- /dev/null +++ b/model_chat.py @@ -0,0 +1,100 @@ +import argparse +import random +import warnings +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): + transformers_model_path = './MiniMind2' + tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) + model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) + 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(args.device), tokenizer + + +# 设置可复现的随机种子 +def setup_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def main(): + print("开始加载模型") + parser = argparse.ArgumentParser(description="Chat with MiniMind") + parser.add_argument('--lora_name', default='None', type=str) + parser.add_argument('--out_dir', default='out', type=str) + 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=512, 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) + # 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('--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") + args = parser.parse_args() + + model, tokenizer = init_model(args) + + streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) + + messages = [] + for prompt in iter(lambda: input('👶: '), ''): + if prompt == "exit": + break + setup_seed(random.randint(0, 2048)) + + messages.append({"role": "user", "content": prompt}) + + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) if args.model_mode != 0 else (tokenizer.bos_token + prompt) + + inputs = tokenizer( + new_prompt, + return_tensors="pt", + truncation=True + ).to(args.device) + + 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 + ) + + response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) + messages.append({"role": "assistant", "content": response}) + print('\n') + + +if __name__ == "__main__": + main() From 60fa1cc6350f6663362ed99f2ad39a8f09f36bb7 Mon Sep 17 00:00:00 2001 From: math-zhuxy <235934429@qq.com> Date: Sun, 25 May 2025 18:46:42 +0800 Subject: [PATCH 2/4] done --- README.md | 22 +++++++++ model_chat.py | 125 +++++++++++++++++++++----------------------------- 2 files changed, 74 insertions(+), 73 deletions(-) diff --git a/README.md b/README.md index 8ef9609..176b40b 100644 --- a/README.md +++ b/README.md @@ -235,6 +235,18 @@ git clone https://github.com/jingyaogong/minimind.git pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` +因为需要的库较多，建议使用虚拟环境来安装： +```bash +python -m venv .venv +``` + +再下载库： + +```bash +pip install -r requirements.txt +``` + + ### 2.下载模型 到项目根目录 ```bash @@ -243,11 +255,21 @@ git clone https://huggingface.co/jingyaogong/MiniMind2 ### （可选）命令行问答 +如果在第1步中安装了虚拟环境，需要在使用命令行激活虚拟环境： +```bash +.venv/Scripts/activate +``` + ```bash # load=0: load from pytorch model, load=1: load from transformers-hf model python eval_model.py --load 1 --model_mode 2 ``` +或者使用 +```bash +python model_chat.py +``` + ### （可选）启动WebUI ```bash diff --git a/model_chat.py b/model_chat.py index 6a08f19..ed609ac 100644 --- a/model_chat.py +++ b/model_chat.py @@ -1,21 +1,17 @@ +print("开始加载模型") + import argparse import random -import warnings import numpy as np -from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer -from model.model_minimind import MiniMindConfig, MiniMindForCausalLM -from model.model_lora import * +from transformers import AutoTokenizer, TextStreamer, LlamaForCausalLM +import torch -warnings.filterwarnings('ignore') - - -def init_model(args): +def init_model(): transformers_model_path = './MiniMind2' tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) - model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) - 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(args.device), tokenizer - + model = LlamaForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) + print(f'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('cuda' if torch.cuda.is_available() else 'cpu'), tokenizer # 设置可复现的随机种子 def setup_seed(seed): @@ -27,74 +23,57 @@ def setup_seed(seed): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False +parser = argparse.ArgumentParser(description="Chat with MiniMind") +parser.add_argument('--temperature', default=0.85, type=float) +parser.add_argument('--top_p', default=0.85, type=float) -def main(): - print("开始加载模型") - parser = argparse.ArgumentParser(description="Chat with MiniMind") - parser.add_argument('--lora_name', default='None', type=str) - parser.add_argument('--out_dir', default='out', type=str) - 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=512, 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) - # 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('--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") - args = parser.parse_args() +parser.add_argument('--max_seq_len', default=8192, type=int) +args = parser.parse_args() +model, tokenizer = init_model() - model, tokenizer = init_model(args) +streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) - streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) +print("模型加载完毕") - messages = [] - for prompt in iter(lambda: input('👶: '), ''): - if prompt == "exit": - break - setup_seed(random.randint(0, 2048)) +messages = [] +for prompt in iter(lambda: input('👶: '), ''): + if prompt == "exit": + break - messages.append({"role": "user", "content": prompt}) + elif print == "clear history": + messages = [] + print("成功清除对话历史") + continue + setup_seed(random.randint(0, 2048)) - new_prompt = tokenizer.apply_chat_template( - messages, - tokenize=False, - add_generation_prompt=True - ) if args.model_mode != 0 else (tokenizer.bos_token + prompt) + messages.append({"role": "user", "content": prompt}) - inputs = tokenizer( - new_prompt, - return_tensors="pt", - truncation=True - ).to(args.device) + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) - 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 - ) + inputs = tokenizer( + new_prompt, + return_tensors="pt", + truncation=True + ).to('cuda' if torch.cuda.is_available() else 'cpu') - response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) - messages.append({"role": "assistant", "content": response}) - 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 + ) - -if __name__ == "__main__": - main() + response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) + messages.append({"role": "assistant", "content": response}) + print('\n') \ No newline at end of file From 4a0975e94e65df3b5764da7401f1bd267e0b4596 Mon Sep 17 00:00:00 2001 From: math-zhuxy <235934429@qq.com> Date: Sun, 25 May 2025 19:50:53 +0800 Subject: [PATCH 3/4] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E4=BA=86=E4=B8=80?= =?UTF-8?q?=E4=BA=9Bbug=E5=92=8C=E6=BC=8F=E6=B4=9E?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 2 +- model/model_minimind.py | 1 - model_chat.py | 18 ++++++++++++------ trainer/train_lora.py | 24 +++++++++++++++++++----- 4 files changed, 32 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index 176b40b..a70d931 100644 --- a/README.md +++ b/README.md @@ -473,7 +473,7 @@ quality（当然也还不算high，提升数据质量无止尽）。 导出文件为`sft_512.jsonl`(~7.5GB)。 [Magpie-SFT数据集](https://www.modelscope.cn/organization/Magpie-Align) -收集了~1M条来自Qwen2/2.5的高质量对话，我将这部分数据进一步清洗，把总长度`<2048`的部分导出为`sft_2048.jsonl`(~9GB)。 +收集了大约1M条来自Qwen2/2.5的高质量对话，我将这部分数据进一步清洗，把总长度`<2048`的部分导出为`sft_2048.jsonl`(~9GB)。 长度`<1024`的部分导出为`sft_1024.jsonl`(~5.5GB)，用大模型对话数据直接进行sft就属于“黑盒蒸馏”的范畴。 进一步清洗前两步sft的数据（只保留中文字符占比高的内容），筛选长度`<512`的对话，得到`sft_mini_512.jsonl`(~1.2GB)。 diff --git a/model/model_minimind.py b/model/model_minimind.py index 4b43610..250b4d1 100644 --- a/model/model_minimind.py +++ b/model/model_minimind.py @@ -290,7 +290,6 @@ class MOEFeedForward(nn.Module): 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]) diff --git a/model_chat.py b/model_chat.py index ed609ac..bdd8337 100644 --- a/model_chat.py +++ b/model_chat.py @@ -10,7 +10,7 @@ def init_model(): transformers_model_path = './MiniMind2' tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) model = LlamaForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) - print(f'模型参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') + print(f'模型参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad)}') return model.eval().to('cuda' if torch.cuda.is_available() else 'cpu'), tokenizer # 设置可复现的随机种子 @@ -24,10 +24,10 @@ def setup_seed(seed): torch.backends.cudnn.benchmark = False parser = argparse.ArgumentParser(description="Chat with MiniMind") -parser.add_argument('--temperature', default=0.85, type=float) +parser.add_argument('--temp', default=0.85, type=float) parser.add_argument('--top_p', default=0.85, type=float) -parser.add_argument('--max_seq_len', default=8192, type=int) +parser.add_argument('--max_len', default=8192, type=int) args = parser.parse_args() model, tokenizer = init_model() @@ -36,6 +36,13 @@ streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) print("模型加载完毕") messages = [] + +random_seed = random.randint(0, 2048) + +setup_seed(random_seed) + +print("使用的随机种子为：", random_seed) + for prompt in iter(lambda: input('👶: '), ''): if prompt == "exit": break @@ -44,7 +51,6 @@ for prompt in iter(lambda: input('👶: '), ''): messages = [] print("成功清除对话历史") continue - setup_seed(random.randint(0, 2048)) messages.append({"role": "user", "content": prompt}) @@ -63,7 +69,7 @@ for prompt in iter(lambda: input('👶: '), ''): print('🤖️: ', end='') generated_ids = model.generate( inputs["input_ids"], - max_new_tokens=args.max_seq_len, + max_new_tokens=args.max_len, num_return_sequences=1, do_sample=True, attention_mask=inputs["attention_mask"], @@ -71,7 +77,7 @@ for prompt in iter(lambda: input('👶: '), ''): eos_token_id=tokenizer.eos_token_id, streamer=streamer, top_p=args.top_p, - temperature=args.temperature + temperature=args.temp ) response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) diff --git a/trainer/train_lora.py b/trainer/train_lora.py index 4711062..3a799f0 100644 --- a/trainer/train_lora.py +++ b/trainer/train_lora.py @@ -1,6 +1,8 @@ import os import sys +from sympy import true + __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) @@ -13,7 +15,7 @@ from torch import optim, nn import torch.distributed as dist from contextlib import nullcontext from torch.utils.data import DataLoader, DistributedSampler -from transformers import AutoTokenizer, AutoModelForCausalLM +from transformers import AutoTokenizer from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset from model.model_lora import load_lora, save_lora, apply_lora @@ -91,8 +93,14 @@ def train_epoch(epoch, wandb): def init_model(lm_config): - tokenizer = AutoTokenizer.from_pretrained('../model/') + current_dir = os.path.dirname(os.path.abspath(__file__)) + model_path = os.path.join(current_dir, '..', 'model') + tokenizer = AutoTokenizer.from_pretrained(model_path) model = MiniMindForCausalLM(lm_config) + if args.minimind2: + model_data_path = os.path.join(current_dir, '..', 'MiniMind2') + model.from_pretrained(model_data_path) + return model.to(args.device), tokenizer moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) @@ -105,9 +113,7 @@ def init_distributed_mode(): global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") - ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) - ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) @@ -136,8 +142,16 @@ if __name__ == "__main__": parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/lora_medical.jsonl") parser.add_argument("--lora_name", type=str, default="lora_medical", help="根据任务保存成lora_(英文/医学/心理...)") + parser.add_argument("--minimind2", type=bool, default=true, help="是否使用从huggingface下载下来的MiniMind2模型") args = parser.parse_args() + if args.minimind2 == true: + args.hidden_size = 768 + args.num_hidden_layers=16 + current_dir = os.path.dirname(os.path.abspath(__file__)) + args.data_path = os.path.join(current_dir, "../dataset/lora_medical.jsonl") + + lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) @@ -201,7 +215,7 @@ if __name__ == "__main__": sampler=train_sampler ) - scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) + scaler = torch.cuda.amp.GradScaler("cuda", enabled=(args.dtype in ['float16', 'bfloat16'])) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): From 3cc6d0d074e3b020e183b2c8ef6703677989ec19 Mon Sep 17 00:00:00 2001 From: Lycoris <149505619+math-zhuxy@users.noreply.github.com> Date: Mon, 26 May 2025 18:30:46 +0800 Subject: [PATCH 4/4] Update train_lora.py --- trainer/train_lora.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/trainer/train_lora.py b/trainer/train_lora.py index 3a799f0..096a63c 100644 --- a/trainer/train_lora.py +++ b/trainer/train_lora.py @@ -19,6 +19,7 @@ from transformers import AutoTokenizer from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset from model.model_lora import load_lora, save_lora, apply_lora +import safesentors.torch warnings.filterwarnings('ignore') @@ -99,7 +100,8 @@ def init_model(lm_config): model = MiniMindForCausalLM(lm_config) if args.minimind2: model_data_path = os.path.join(current_dir, '..', 'MiniMind2') - model.from_pretrained(model_data_path) + state_dict = safetensors.torch.load_file("model.safetensors", device=args.device) + model.load_state_dict(state_dict, strict= True) return model.to(args.device), tokenizer moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth'