Merge #1525 from hiyouga/dev, fix #224 #336 #931 #936 #1011

Refactor llmtuner, support full-parameter RLHF

Former-commit-id: f04bc2a42815d02c22935fec8bf7b81438c8ba79

README.md

@@ -14,7 +14,9 @@
 
 ## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory
 
-Launch **LLaMA Board** via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet)
+Preview LLaMA Board at **[Hugging Face Space](https://huggingface.co/spaces/hiyouga/LLaMA-Board)**.
+
+Launch LLaMA Board via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet)
 
 Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU.
 
@@ -71,7 +73,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 >
 > For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models.
 
-Please refer to [template.py](src/llmtuner/extras/template.py) for a full list of models we supported.
+Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.
 
 ## Supported Training Approaches
 
@@ -79,9 +81,9 @@ Please refer to [template.py](src/llmtuner/extras/template.py) for a full list o
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | Pre-Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| Reward Modeling        |                    |                    | :white_check_mark: | :white_check_mark: |
+| Reward Modeling        | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| PPO Training           |                    |                    | :white_check_mark: | :white_check_mark: |
+| PPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| DPO Training           | :white_check_mark: |                    | :white_check_mark: | :white_check_mark: |
+| DPO Training           | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 
 > [!NOTE]
 > Use `--quantization_bit 4/8` argument to enable QLoRA.
@@ -122,6 +124,7 @@ Please refer to [template.py](src/llmtuner/extras/template.py) for a full list o
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
 - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
 - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
 - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)

README_zh.md

@@ -14,7 +14,9 @@
 
 ## LLaMA Board: 通过一站式网页界面快速上手 LLaMA Factory
 
-使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 **LLaMA Board**。（该界面目前仅支持单卡训练）
+通过 **[Hugging Face Space](https://huggingface.co/spaces/hiyouga/LLaMA-Board)** 预览 LLaMA Board。
+
+使用 `CUDA_VISIBLE_DEVICES=0 python src/train_web.py` 启动 LLaMA Board。（该界面目前仅支持单卡训练）
 
 下面是使用单张 GPU 在 10 分钟内更改对话式大型语言模型自我认知的示例。
 
@@ -71,7 +73,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 >
 > 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用**对应的模板**。
 
-项目所支持模型的完整列表请参阅 [template.py](src/llmtuner/extras/template.py)。
+项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。
 
 ## 训练方法
 
@@ -79,9 +81,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 | ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
 | 预训练                 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 | 指令监督微调            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| 奖励模型训练            |                    |                    | :white_check_mark: | :white_check_mark: |
+| 奖励模型训练            | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| PPO 训练               |                    |                    | :white_check_mark: | :white_check_mark: |
+| PPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
-| DPO 训练               | :white_check_mark: |                    | :white_check_mark: | :white_check_mark: |
+| DPO 训练               | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
 
 > [!NOTE]
 > 请使用 `--quantization_bit 4/8` 参数来启用 QLoRA 训练。
@@ -122,6 +124,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846
 - [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
 - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
 - [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
 - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
 - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)

data/belle_multiturn/belle_multiturn.py

@@ -24,9 +24,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
 
     def _info(self):
         features = datasets.Features({
-            "instruction": datasets.Value("string"),
+            "conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
-            "output": datasets.Value("string"),
-            "history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
         })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -51,6 +49,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
         with open(filepath, "r", encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)
+                conversations = []
                 prompt = data["instruction"].strip()
                 response = data["output"].strip()
 
@@ -58,7 +57,8 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                 human_idx = prompt.rfind("Human:")
                 query = prompt[human_idx+6:assist_idx].strip()
                 prompt = prompt[:human_idx].strip()
-                history = []
+                conversations.insert(0, {"from": "gpt", "value": response})
+                conversations.insert(0, {"from": "human", "value": query})
 
                 while prompt.rfind("Assistant:") != -1:
                     assist_idx = prompt.rfind("Assistant:")
@@ -66,13 +66,10 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
                     if human_idx != -1:
                         old_query = prompt[human_idx+6:assist_idx].strip()
                         old_resp = prompt[assist_idx+10:].strip()
-                        history.insert(0, (old_query, old_resp))
+                        conversations.insert(0, {"from": "gpt", "value": old_resp})
+                        conversations.insert(0, {"from": "human", "value": old_query})
                     else:
                         break
                     prompt = prompt[:human_idx].strip()
 
-                yield key, {
+                yield key, {"conversations": conversations}
-                    "instruction": query,
-                    "output": response,
-                    "history": history
-                }

data/dataset_info.json

@@ -88,11 +88,7 @@
   },
   "belle_multiturn": {
     "script_url": "belle_multiturn",
-    "columns": {
+    "formatting": "sharegpt"
-      "prompt": "instruction",
-      "response": "output",
-      "history": "history"
-    }
   },
   "ultra_chat": {
     "script_url": "ultra_chat",
@@ -107,6 +103,13 @@
   "alpaca_cot": {
     "hf_hub_url": "QingyiSi/Alpaca-CoT"
   },
+  "openorca": {
+    "hf_hub_url": "Open-Orca/OpenOrca",
+    "columns": {
+      "prompt": "question",
+      "response": "response"
+    }
+  },
   "mathinstruct": {
     "hf_hub_url": "TIGER-Lab/MathInstruct",
     "columns": {

data/ultra_chat/ultra_chat.py

@@ -66,6 +66,4 @@ class UltraChat(datasets.GeneratorBasedBuilder):
                         "from": "human" if i % 2 == 0 else "gpt",
                         "value": content[i]
                     } for i in range(len(content))]
-                    yield key, {
+                    yield key, {"conversations": conversations}
-                        "conversations": conversations
-                    }

src/llmtuner/__init__.py

@@ -1,9 +1,9 @@
-# Level: api, webui > chat, eval > tuner > dsets > extras, hparams
+# Level: api, webui > chat, eval, train > data, model > extras, hparams
 
 from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
 from llmtuner.eval import Evaluator
-from llmtuner.tuner import export_model, run_exp
+from llmtuner.train import export_model, run_exp
 from llmtuner.webui import create_ui, create_web_demo
 
 

src/llmtuner/api/app.py

@@ -1,14 +1,8 @@
 import json
-import uvicorn
-from fastapi import FastAPI, HTTPException, status
-from fastapi.middleware.cors import CORSMiddleware
-from contextlib import asynccontextmanager
-from sse_starlette import EventSourceResponse
 from typing import List, Tuple
 from pydantic import BaseModel
+from contextlib import asynccontextmanager
 
-from llmtuner.extras.misc import torch_gc
-from llmtuner.chat import ChatModel
 from llmtuner.api.protocol import (
     Role,
     Finish,
@@ -23,10 +17,28 @@ from llmtuner.api.protocol import (
     ChatCompletionResponseStreamChoice,
     ChatCompletionResponseUsage
 )
+from llmtuner.chat import ChatModel
+from llmtuner.extras.misc import torch_gc
+from llmtuner.extras.packages import (
+    is_fastapi_availble, is_starlette_available, is_uvicorn_available
+)
+
+
+if is_fastapi_availble():
+    from fastapi import FastAPI, HTTPException, status
+    from fastapi.middleware.cors import CORSMiddleware
+
+
+if is_starlette_available():
+    from sse_starlette import EventSourceResponse
+
+
+if is_uvicorn_available():
+    import uvicorn
 
 
 @asynccontextmanager
-async def lifespan(app: FastAPI): # collects GPU memory
+async def lifespan(app: "FastAPI"): # collects GPU memory
     yield
     torch_gc()
 
@@ -38,7 +50,7 @@ def to_json(data: BaseModel) -> str:
         return data.json(exclude_unset=True, ensure_ascii=False)
 
 
-def create_app(chat_model: ChatModel) -> FastAPI:
+def create_app(chat_model: "ChatModel") -> "FastAPI":
     app = FastAPI(lifespan=lifespan)
 
     app.add_middleware(
@@ -56,12 +68,12 @@ def create_app(chat_model: ChatModel) -> FastAPI:
 
     @app.post("/v1/chat/completions", response_model=ChatCompletionResponse, status_code=status.HTTP_200_OK)
     async def create_chat_completion(request: ChatCompletionRequest):
-        if len(request.messages) < 1 or request.messages[-1].role != Role.USER:
+        if len(request.messages) == 0 or request.messages[-1].role != Role.USER:
             raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")
 
         query = request.messages[-1].content
         prev_messages = request.messages[:-1]
-        if len(prev_messages) > 0 and prev_messages[0].role == Role.SYSTEM:
+        if len(prev_messages) and prev_messages[0].role == Role.SYSTEM:
             system = prev_messages.pop(0).content
         else:
             system = None
@@ -73,12 +85,14 @@ def create_app(chat_model: ChatModel) -> FastAPI:
                     history.append([prev_messages[i].content, prev_messages[i+1].content])
                 else:
                     raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
+        else:
+            raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
 
         if request.stream:
             generate = predict(query, history, system, request)
             return EventSourceResponse(generate, media_type="text/event-stream")
 
-        response, (prompt_length, response_length) = chat_model.chat(
+        responses = chat_model.chat(
             query, history, system,
             do_sample=request.do_sample,
             temperature=request.temperature,
@@ -87,18 +101,23 @@ def create_app(chat_model: ChatModel) -> FastAPI:
             num_return_sequences=request.n
         )
 
+        prompt_length, response_length = 0, 0
+        choices = []
+        for i, response in enumerate(responses):
+            choices.append(ChatCompletionResponseChoice(
+                index=i,
+                message=ChatMessage(role=Role.ASSISTANT, content=response.response_text),
+                finish_reason=Finish.STOP if response.finish_reason == "stop" else Finish.LENGTH
+            ))
+            prompt_length = response.prompt_length
+            response_length += response.response_length
+
         usage = ChatCompletionResponseUsage(
             prompt_tokens=prompt_length,
             completion_tokens=response_length,
             total_tokens=prompt_length+response_length
         )
 
-        choices = [ChatCompletionResponseChoice(
-            index=i,
-            message=ChatMessage(role=Role.ASSISTANT, content=choice),
-            finish_reason=Finish.STOP
-        ) for i, choice in enumerate(response)]
-
         return ChatCompletionResponse(model=request.model, choices=choices, usage=usage)
 
     async def predict(query: str, history: List[Tuple[str, str]], system: str, request: ChatCompletionRequest):

src/llmtuner/chat/__init__.py

@@ -1 +1 @@
-from llmtuner.chat.stream_chat import ChatModel
+from llmtuner.chat.chat_model import ChatModel

src/llmtuner/chat/chat_model.py

@@ -1,11 +1,21 @@
 import torch
-from typing import Any, Dict, Generator, List, Optional, Tuple
+from dataclasses import dataclass
+from typing import Any, Dict, Generator, List, Literal, Optional, Tuple
 from threading import Thread
 from transformers import GenerationConfig, TextIteratorStreamer
 
-from llmtuner.extras.misc import dispatch_model, get_logits_processor
+from llmtuner.data.template import get_template_and_fix_tokenizer
-from llmtuner.extras.template import get_template_and_fix_tokenizer
+from llmtuner.extras.misc import get_logits_processor
-from llmtuner.tuner.core import get_infer_args, load_model_and_tokenizer
+from llmtuner.model import dispatch_model, get_infer_args, load_model_and_tokenizer
+
+
+@dataclass
+class Response:
+
+    response_text: str
+    response_length: int
+    prompt_length: int
+    finish_reason: Literal["stop", "length"]
 
 
 class ChatModel:
@@ -18,7 +28,7 @@ class ChatModel:
         self.template = get_template_and_fix_tokenizer(data_args.template, self.tokenizer)
         self.system_prompt = data_args.system_prompt
 
-    def process_args(
+    def _process_args(
         self,
         query: str,
         history: Optional[List[Tuple[str, str]]] = None,
@@ -79,17 +89,30 @@ class ChatModel:
         history: Optional[List[Tuple[str, str]]] = None,
         system: Optional[str] = None,
         **input_kwargs
-    ) -> Tuple[List[str], Tuple[int, int]]:
+    ) -> List[Response]:
-        gen_kwargs, prompt_length = self.process_args(query, history, system, **input_kwargs)
+        r"""
+        Args: query, history, system, **input_kwargs
+
+        Returns: [(response_text, prompt_length, response_length)] * n (default n=1)
+        """
+        gen_kwargs, prompt_length = self._process_args(query, history, system, **input_kwargs)
         generate_output = self.model.generate(**gen_kwargs)
         response_ids = generate_output[:, prompt_length:]
-        response = self.tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+        response = self.tokenizer.batch_decode(
-        response_length = 0
+            response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
-        for i in range(len(response_ids)):
+        )
+        results = []
+        for i in range(len(response)):
             eos_index = (response_ids[i] == self.tokenizer.eos_token_id).nonzero()
-            response_length += eos_index[0].item() if len(eos_index) else len(response_ids[i])
+            response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
+            results.append(Response(
+                response_text=response[i],
+                response_length=response_length,
+                prompt_length=prompt_length,
+                finish_reason="stop" if len(eos_index) else "length"
+            ))
 
-        return response, (prompt_length, response_length)
+        return results
 
     @torch.inference_mode()
     def stream_chat(
@@ -99,7 +122,7 @@ class ChatModel:
         system: Optional[str] = None,
         **input_kwargs
     ) -> Generator[str, None, None]:
-        gen_kwargs, _ = self.process_args(query, history, system, **input_kwargs)
+        gen_kwargs, _ = self._process_args(query, history, system, **input_kwargs)
         streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
         gen_kwargs["streamer"] = streamer
 

src/llmtuner/data/__init__.py

@@ -0,0 +1,4 @@
+from llmtuner.data.loader import get_dataset
+from llmtuner.data.preprocess import preprocess_dataset
+from llmtuner.data.template import get_template_and_fix_tokenizer
+from llmtuner.data.utils import split_dataset

src/llmtuner/data/loader.py

@@ -3,7 +3,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Union
 
 from datasets import concatenate_datasets, interleave_datasets, load_dataset
 
-from llmtuner.dsets.utils import checksum, EXT2TYPE
+from llmtuner.data.utils import checksum, EXT2TYPE
 from llmtuner.extras.logging import get_logger
 
 if TYPE_CHECKING:

src/llmtuner/data/preprocess.py

@@ -5,9 +5,9 @@ from typing import TYPE_CHECKING, Any, Dict, Generator, List, Literal, Tuple, Un
 
 from datasets import load_from_disk
 
+from llmtuner.data.template import get_template_and_fix_tokenizer
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.logging import get_logger
-from llmtuner.extras.template import get_template_and_fix_tokenizer
 
 if TYPE_CHECKING:
     from datasets import Dataset, IterableDataset

src/llmtuner/data/template.py

@@ -225,9 +225,6 @@ def get_template_and_fix_tokenizer(
     return template
 
 
-r"""
-Supports: https://huggingface.co/tatsu-lab/alpaca-7b-wdiff
-"""
 register_template(
     name="alpaca",
     prefix=[
@@ -246,11 +243,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/BAAI/AquilaChat-7B
-          https://huggingface.co/BAAI/AquilaChat2-7B
-          https://huggingface.co/BAAI/AquilaChat2-34B
-"""
 register_template(
     name="aquila",
     prefix=[
@@ -273,9 +265,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat
-"""
 register_template(
     name="baichuan",
     prefix=[
@@ -292,10 +281,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat
-          https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat
-"""
 register_template(
     name="baichuan2",
     prefix=[
@@ -312,9 +297,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/BelleGroup/BELLE-LLaMA-EXT-13B
-"""
 register_template(
     name="belle",
     prefix=[
@@ -330,9 +312,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/vivo-ai/BlueLM-7B-Chat
-"""
 register_template(
     name="bluelm",
     prefix=[
@@ -348,9 +327,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/THUDM/chatglm2-6b
-"""
 register_template(
     name="chatglm2",
     prefix=[
@@ -369,9 +345,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/THUDM/chatglm3-6b
-"""
 register_template(
     name="chatglm3",
     prefix=[
@@ -395,11 +368,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct
-          https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct
-          https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct
-"""
 register_template(
     name="deepseek",
     prefix=[
@@ -426,9 +394,6 @@ register_template(
 )
 
 
-r"""
-Default template.
-"""
 register_template(
     name="default",
     prefix=[
@@ -447,9 +412,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/tiiuae/falcon-180B-chat
-"""
 register_template(
     name="falcon",
     prefix=[
@@ -466,10 +428,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/internlm/internlm-chat-7b
-          https://huggingface.co/internlm/internlm-chat-20b
-"""
 register_template(
     name="intern",
     prefix=[
@@ -492,11 +450,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
-          https://huggingface.co/meta-llama/Llama-2-13b-chat-hf
-          https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
-"""
 register_template(
     name="llama2",
     prefix=[
@@ -519,10 +472,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/ziqingyang/chinese-alpaca-2-7b
-          https://huggingface.co/ziqingyang/chinese-alpaca-2-13b
-"""
 register_template(
     name="llama2_zh",
     prefix=[
@@ -536,9 +485,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1
-"""
 register_template(
     name="mistral",
     prefix=[
@@ -552,9 +498,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/openchat/openchat_3.5
-"""
 register_template(
     name="openchat",
     prefix=[
@@ -576,10 +519,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/Qwen/Qwen-7B-Chat
-          https://huggingface.co/Qwen/Qwen-14B-Chat
-"""
 register_template(
     name="qwen",
     prefix=[
@@ -606,10 +545,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/HuggingFaceH4/starchat-alpha
-          https://huggingface.co/HuggingFaceH4/starchat-beta
-"""
 register_template(
     name="starchat",
     prefix=[
@@ -650,10 +585,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/lmsys/vicuna-7b-v1.5
-          https://huggingface.co/lmsys/vicuna-13b-v1.5
-"""
 register_template(
     name="vicuna",
     prefix=[
@@ -670,10 +601,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/xverse/XVERSE-7B-Chat
-          https://huggingface.co/xverse/XVERSE-13B-Chat
-"""
 register_template(
     name="xverse",
     prefix=[
@@ -687,11 +614,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/wenge-research/yayi-7b
-          https://huggingface.co/wenge-research/yayi-7b-llama2
-          https://huggingface.co/wenge-research/yayi-13b-llama2
-"""
 register_template(
     name="yayi",
     prefix=[
@@ -724,10 +646,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha
-          https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
-"""
 register_template(
     name="zephyr",
     prefix=[
@@ -746,11 +664,6 @@ register_template(
 )
 
 
-r"""
-Supports: https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1
-          https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1
-          https://huggingface.co/IDEA-CCNL/Ziya2-13B-Chat
-"""
 register_template(
     name="ziya",
     prefix=[

src/llmtuner/dsets/__init__.py

@@ -1,3 +0,0 @@
-from llmtuner.dsets.loader import get_dataset
-from llmtuner.dsets.preprocess import preprocess_dataset
-from llmtuner.dsets.utils import split_dataset

src/llmtuner/eval/__init__.py

@@ -1 +1 @@
-from llmtuner.eval.engine import Evaluator
+from llmtuner.eval.evaluator import Evaluator

src/llmtuner/eval/constants.py

@@ -1,3 +0,0 @@
-CHOICES = ["A", "B", "C", "D"]
-
-SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]

src/llmtuner/eval/evaluator.py

@@ -11,12 +11,10 @@ from typing import Any, Dict, List, Optional
 from datasets import load_dataset
 from transformers.utils import cached_file
 
-from llmtuner.eval.constants import CHOICES, SUBJECTS
+from llmtuner.data.template import get_template_and_fix_tokenizer
-from llmtuner.eval.parser import get_eval_args
 from llmtuner.eval.template import get_eval_template
-from llmtuner.extras.misc import dispatch_model
+from llmtuner.extras.constants import CHOICES, SUBJECTS
-from llmtuner.extras.template import get_template_and_fix_tokenizer
+from llmtuner.model import dispatch_model, get_eval_args, load_model_and_tokenizer
-from llmtuner.tuner.core import load_model_and_tokenizer
 
 
 class Evaluator:

src/llmtuner/eval/parser.py

@@ -1,49 +0,0 @@
-import transformers
-from typing import Any, Dict, Optional, Tuple
-from transformers import HfArgumentParser
-
-from llmtuner.extras.misc import parse_args
-from llmtuner.hparams import (
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-)
-
-
-def parse_eval_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-]:
-    parser = HfArgumentParser((
-        ModelArguments,
-        DataArguments,
-        EvaluationArguments,
-        FinetuningArguments
-    ))
-    return parse_args(parser, args)
-
-
-def get_eval_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-]:
-    model_args, data_args, eval_args, finetuning_args = parse_eval_args(args)
-
-    if data_args.template is None:
-        raise ValueError("Please specify which `template` to use.")
-
-    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
-        raise ValueError("Quantization is only compatible with the LoRA method.")
-
-    transformers.set_seed(eval_args.seed)
-
-    return model_args, data_args, eval_args, finetuning_args

src/llmtuner/eval/template.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, List, Tuple
 
-from llmtuner.eval.constants import CHOICES
+from llmtuner.extras.constants import CHOICES
 
 if TYPE_CHECKING:
     from datasets import Dataset

src/llmtuner/extras/callbacks.py

@@ -12,6 +12,7 @@ from llmtuner.extras.logging import get_logger
 
 if TYPE_CHECKING:
     from transformers import TrainingArguments, TrainerState, TrainerControl
+    from trl import AutoModelForCausalLMWithValueHead
 
 
 logger = get_logger(__name__)
@@ -25,18 +26,24 @@ class SavePeftModelCallback(TrainerCallback):
         """
         if args.should_save:
             output_dir = os.path.join(args.output_dir, "{}-{}".format(PREFIX_CHECKPOINT_DIR, state.global_step))
-            model = kwargs.pop("model")
+            model: "AutoModelForCausalLMWithValueHead" = kwargs.pop("model")
+            model.pretrained_model.config.save_pretrained(output_dir)
+            if model.pretrained_model.can_generate():
+                model.pretrained_model.generation_config.save_pretrained(output_dir)
             if getattr(model, "is_peft_model", False):
-                getattr(model, "pretrained_model").save_pretrained(output_dir)
+                model.pretrained_model.save_pretrained(output_dir)
 
     def on_train_end(self, args: "TrainingArguments", state: "TrainerState", control: "TrainerControl", **kwargs):
         r"""
         Event called at the end of training.
         """
         if args.should_save:
-            model = kwargs.pop("model")
+            model: "AutoModelForCausalLMWithValueHead" = kwargs.pop("model")
+            model.pretrained_model.config.save_pretrained(args.output_dir)
+            if model.pretrained_model.can_generate():
+                model.pretrained_model.generation_config.save_pretrained(args.output_dir)
             if getattr(model, "is_peft_model", False):
-                getattr(model, "pretrained_model").save_pretrained(args.output_dir)
+                model.pretrained_model.save_pretrained(args.output_dir)
 
 
 class LogCallback(TrainerCallback):

src/llmtuner/extras/constants.py

@@ -2,12 +2,24 @@ from collections import defaultdict, OrderedDict
 from typing import Dict, Optional
 
 
+CHOICES = ["A", "B", "C", "D"]
+
+DEFAULT_MODULE = defaultdict(str)
+
+DEFAULT_TEMPLATE = defaultdict(str)
+
 IGNORE_INDEX = -100
 
+LAYERNORM_NAMES = {"norm", "ln"}
+
 LOG_FILE_NAME = "trainer_log.jsonl"
 
 METHODS = ["full", "freeze", "lora"]
 
+SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
+
+SUPPORTED_MODELS = OrderedDict()
+
 TRAINING_STAGES = {
     "Supervised Fine-Tuning": "sft",
     "Reward Modeling": "rm",
@@ -16,14 +28,6 @@ TRAINING_STAGES = {
     "Pre-Training": "pt"
 }
 
-LAYERNORM_NAMES = {"norm", "ln"}
-
-SUPPORTED_MODELS = OrderedDict()
-
-DEFAULT_MODULE = defaultdict(str)
-
-DEFAULT_TEMPLATE = defaultdict(str)
-
 
 def register_model_group(
     models: Dict[str, str],
@@ -116,10 +120,12 @@ register_model_group(
 
 register_model_group(
     models={
-        "ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b",
+        "ChineseLLaMA2-1.3B": "hfl/chinese-llama-2-1.3b",
-        "ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b",
+        "ChineseLLaMA2-7B": "hfl/chinese-llama-2-7b",
-        "ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b",
+        "ChineseLLaMA2-13B": "hfl/chinese-llama-2-13b",
-        "ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b"
+        "ChineseLLaMA2-1.3B-Chat": "hfl/chinese-alpaca-2-1.3b",
+        "ChineseLLaMA2-7B-Chat": "hfl/chinese-alpaca-2-7b",
+        "ChineseLLaMA2-13B-Chat": "hfl/chinese-alpaca-2-13b"
     },
     template="llama2_zh"
 )
@@ -190,6 +196,14 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "OpenChat3.5-7B-Chat": "openchat/openchat_3.5"
+    },
+    template="openchat"
+)
+
+
 register_model_group(
     models={
         "Phi1.5-1.3B": "microsoft/phi-1_5"
@@ -217,6 +231,15 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "Vicuna1.5-7B-Chat": "lmsys/vicuna-7b-v1.5",
+        "Vicuna1.5-13B-Chat": "lmsys/vicuna-13b-v1.5"
+    },
+    template="vicuna"
+)
+
+
 register_model_group(
     models={
         "XVERSE-7B": "xverse/XVERSE-7B",
@@ -229,9 +252,27 @@ register_model_group(
 )
 
 
+register_model_group(
+    models={
+        "Yayi-7B": "wenge-research/yayi-7b-llama2",
+        "Yayi-13B": "wenge-research/yayi-13b-llama2"
+    },
+    template="yayi"
+)
+
+
 register_model_group(
     models={
         "Yi-6B": "01-ai/Yi-6B",
         "Yi-34B": "01-ai/Yi-34B"
     }
 )
+
+
+register_model_group(
+    models={
+        "Zephyr-7B-Alpha-Chat": "HuggingFaceH4/zephyr-7b-alpha",
+        "Zephyr-7B-Beta-Chat": "HuggingFaceH4/zephyr-7b-beta"
+    },
+    template="zephyr"
+)

src/llmtuner/extras/logging.py

@@ -3,6 +3,9 @@ import logging
 
 
 class LoggerHandler(logging.Handler):
+    r"""
+    Logger handler used in Web UI.
+    """
 
     def __init__(self):
         super().__init__()
@@ -19,16 +22,10 @@ class LoggerHandler(logging.Handler):
         self.log += "\n\n"
 
 
-def reset_logging():
-    r"""
-    Removes basic config of root logger
-    """
-    root = logging.getLogger()
-    list(map(root.removeHandler, root.handlers))
-    list(map(root.removeFilter, root.filters))
-
-
 def get_logger(name: str) -> logging.Logger:
+    r"""
+    Gets a standard logger with a stream hander to stdout.
+    """
     formatter = logging.Formatter(
         fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
         datefmt="%m/%d/%Y %H:%M:%S"
@@ -41,3 +38,12 @@ def get_logger(name: str) -> logging.Logger:
     logger.addHandler(handler)
 
     return logger
+
+
+def reset_logging() -> None:
+    r"""
+    Removes basic config of root logger. (unused in script)
+    """
+    root = logging.getLogger()
+    list(map(root.removeHandler, root.handlers))
+    list(map(root.removeFilter, root.filters))

src/llmtuner/extras/misc.py

@@ -13,14 +13,13 @@ try:
         is_torch_npu_available
     )
     _is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
-    _is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available
+    _is_bf16_available = is_torch_bf16_gpu_available() or is_torch_bf16_cpu_available()
 except ImportError:
     _is_fp16_available = torch.cuda.is_available()
     _is_bf16_available = torch.cuda.is_bf16_supported()
 
 if TYPE_CHECKING:
     from transformers import HfArgumentParser
-    from transformers.modeling_utils import PreTrainedModel
 
 
 class AverageMeter:
@@ -65,16 +64,14 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
     return trainable_params, all_param
 
 
-def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
+def get_current_device() -> str:
-    r"""
+    import accelerate
-    Infers the optimal dtype according to the model_dtype and device compatibility.
+    from accelerate import Accelerator
-    """
+    dummy_accelerator = Accelerator()
-    if _is_bf16_available and model_dtype == torch.bfloat16:
+    if accelerate.utils.is_xpu_available():
-        return torch.bfloat16
+        return "xpu:{}".format(dummy_accelerator.local_process_index)
-    elif _is_fp16_available:
-        return torch.float16
     else:
-        return torch.float32
+        return dummy_accelerator.local_process_index if torch.cuda.is_available() else "cpu"
 
 
 def get_logits_processor() -> "LogitsProcessorList":
@@ -86,14 +83,16 @@ def get_logits_processor() -> "LogitsProcessorList":
     return logits_processor
 
 
-def torch_gc() -> None:
+def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
     r"""
-    Collects GPU memory.
+    Infers the optimal dtype according to the model_dtype and device compatibility.
     """
-    gc.collect()
+    if _is_bf16_available and model_dtype == torch.bfloat16:
-    if torch.cuda.is_available():
+        return torch.bfloat16
-        torch.cuda.empty_cache()
+    elif _is_fp16_available:
-        torch.cuda.ipc_collect()
+        return torch.float16
+    else:
+        return torch.float32
 
 
 def parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None) -> Tuple[Any]:
@@ -107,26 +106,11 @@ def parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = None
         return parser.parse_args_into_dataclasses()
 
 
-def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
+def torch_gc() -> None:
     r"""
-    Dispatches a pre-trained model to GPUs with balanced memory.
+    Collects GPU memory.
-    Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
     """
-    if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing
+    gc.collect()
-        return model
+    if torch.cuda.is_available():
-
+        torch.cuda.empty_cache()
-    if torch.cuda.device_count() > 1:
+        torch.cuda.ipc_collect()
-        from accelerate import dispatch_model
-        from accelerate.utils import infer_auto_device_map, get_balanced_memory
-
-        if model._no_split_modules is None:
-            raise ValueError("The model class needs to implement the `_no_split_modules` attribute.")
-
-        kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules}
-        max_memory = get_balanced_memory(model, **kwargs)
-        # Make sure tied weights are tied before creating the device map.
-        model.tie_weights()
-        device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
-        return dispatch_model(model, device_map)
-    else:
-        return model.cuda()

src/llmtuner/extras/packages.py

@@ -0,0 +1,55 @@
+import importlib.metadata
+import importlib.util
+
+
+def is_package_available(name: str) -> bool:
+    return importlib.util.find_spec(name) is not None
+
+
+def get_package_version(name: str) -> str:
+    try:
+        return importlib.metadata.version(name)
+    except:
+        return "0.0.0"
+
+
+_fastapi_available = is_package_available("fastapi")
+_flash_attn2_available = is_package_available("flash_attn") and get_package_version("flash_attn").startswith("2")
+_jieba_available = is_package_available("jieba")
+_matplotlib_available = is_package_available("matplotlib")
+_nltk_available = is_package_available("nltk")
+_rouge_available = is_package_available("rouge-chinese")
+_starlette_available = is_package_available("sse-starlette")
+_uvicorn_available = is_package_available("uvicorn")
+
+
+def is_fastapi_availble():
+    return _fastapi_available
+
+
+def is_flash_attn2_available():
+    return _flash_attn2_available
+
+
+def is_jieba_available():
+    return _jieba_available
+
+
+def is_matplotlib_available():
+    return _matplotlib_available
+
+
+def is_nltk_available():
+    return _nltk_available
+
+
+def is_rouge_available():
+    return _rouge_available
+
+
+def is_starlette_available():
+    return _starlette_available
+
+
+def is_uvicorn_available():
+    return _uvicorn_available

src/llmtuner/extras/patches/llama_patch.py

@@ -3,16 +3,19 @@ import torch
 import torch.nn as nn
 from typing import Optional, Tuple
 from transformers.utils import logging
-from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv
+from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb
-
-is_flash_attn_2_available = False
 
 try:
+    from transformers.models.llama.modeling_llama import repeat_kv
+except ImportError:
+    print("Please upgrade `transformers`.")
+
+from llmtuner.extras.packages import is_flash_attn2_available
+
+
+if is_flash_attn2_available():
     from flash_attn import flash_attn_func, flash_attn_varlen_func # type: ignore
     from flash_attn.bert_padding import pad_input, unpad_input # type: ignore
-    is_flash_attn_2_available = True
-except ImportError:
-    is_flash_attn_2_available = False
 
 
 logger = logging.get_logger(__name__)

src/llmtuner/extras/ploting.py

@@ -1,11 +1,14 @@
 import os
 import math
 import json
-import matplotlib.pyplot as plt
 from typing import List, Optional
 from transformers.trainer import TRAINER_STATE_NAME
 
 from llmtuner.extras.logging import get_logger
+from llmtuner.extras.packages import is_matplotlib_available
+
+if is_matplotlib_available():
+    import matplotlib.pyplot as plt
 
 
 logger = get_logger(__name__)

src/llmtuner/hparams/finetuning_args.py

@@ -4,38 +4,38 @@ from dataclasses import asdict, dataclass, field
 
 
 @dataclass
-class FinetuningArguments:
+class FreezeArguments:
     r"""
-    Arguments pertaining to which techniques we are going to fine-tuning with.
+    Arguments pertaining to the freeze (partial-parameter) training.
     """
-    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
-        default="sft",
-        metadata={"help": "Which stage will be performed in training."}
-    )
-    finetuning_type: Optional[Literal["lora", "freeze", "full"]] = field(
-        default="lora",
-        metadata={"help": "Which fine-tuning method to use."}
-    )
     num_layer_trainable: Optional[int] = field(
         default=3,
         metadata={"help": "Number of trainable layers for partial-parameter (freeze) fine-tuning."}
     )
-    name_module_trainable: Optional[Literal["mlp", "self_attn", "self_attention"]] = field(
+    name_module_trainable: Optional[str] = field(
         default="mlp",
         metadata={"help": "Name of trainable modules for partial-parameter (freeze) fine-tuning. \
+                  Use commas to separate multiple modules. \
                   LLaMA choices: [\"mlp\", \"self_attn\"], \
                   BLOOM & Falcon & ChatGLM choices: [\"mlp\", \"self_attention\"], \
                   Qwen choices: [\"mlp\", \"attn\"], \
                   Phi-1.5 choices: [\"mlp\", \"mixer\"], \
-                  LLaMA-2, BlueLM, Baichuan, InternLM, Mistral, Skywork, XVERSE, Yi choices: the same as LLaMA."}
+                  Others choices: the same as LLaMA."}
     )
+
+
+@dataclass
+class LoraArguments:
+    r"""
+    Arguments pertaining to the LoRA training.
+    """
     lora_rank: Optional[int] = field(
         default=8,
         metadata={"help": "The intrinsic dimension for LoRA fine-tuning."}
     )
     lora_alpha: Optional[float] = field(
-        default=32.0,
+        default=None,
-        metadata={"help": "The scale factor for LoRA fine-tuning (similar with the learning rate)."}
+        metadata={"help": "The scale factor for LoRA fine-tuning (default: lora_rank * 2.0)."}
     )
     lora_dropout: Optional[float] = field(
         default=0.1,
@@ -49,7 +49,7 @@ class FinetuningArguments:
                   Baichuan choices: [\"W_pack\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"], \
                   Qwen choices: [\"c_attn\", \"attn.c_proj\", \"w1\", \"w2\", \"mlp.c_proj\"], \
                   Phi-1.5 choices: [\"Wqkv\", \"out_proj\", \"fc1\", \"fc2\"], \
-                  LLaMA-2, BlueLM, InternLM, Mistral, Skywork, XVERSE, Yi choices: the same as LLaMA."}
+                  Others choices: the same as LLaMA."}
     )
     additional_target: Optional[str] = field(
         default=None,
@@ -59,30 +59,76 @@ class FinetuningArguments:
         default=True,
         metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
     )
-    ppo_score_norm: Optional[bool] = field(
+
-        default=False,
+
-        metadata={"help": "Use score normalization in PPO training."}
+@dataclass
+class RLHFArguments:
+    r"""
+    Arguments pertaining to the PPO and DPO training.
+    """
+    dpo_beta: Optional[float] = field(
+        default=0.1,
+        metadata={"help": "The beta parameter for the DPO loss."}
     )
     ppo_logger: Optional[str] = field(
         default=None,
         metadata={"help": "Log with either 'wandb' or 'tensorboard' in PPO training."}
     )
+    ppo_score_norm: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Use score normalization in PPO training."}
+    )
     ppo_target: Optional[float] = field(
         default=6.0,
         metadata={"help": "Target KL value for adaptive KL control in PPO training."}
     )
-    dpo_beta: Optional[float] = field(
+    ppo_whiten_rewards: Optional[bool] = field(
-        default=0.1,
+        default=False,
-        metadata={"help": "The beta parameter for the DPO loss."}
+        metadata={"help": "Whiten the rewards before compute advantages in PPO training."}
     )
-    dpo_ref_model: Optional[str] = field(
+    ref_model: Optional[str] = field(
         default=None,
-        metadata={"help": "Path to the reference model used for the DPO training."}
+        metadata={"help": "Path to the reference model used for the PPO or DPO training."}
     )
-    dpo_ref_model_checkpoint: Optional[str] = field(
+    ref_model_checkpoint: Optional[str] = field(
         default=None,
         metadata={"help": "Path to the directory(s) containing the model checkpoints of the reference model."}
     )
+    ref_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reference model."}
+    )
+    reward_model: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
+    )
+    reward_model_checkpoint: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory(s) containing the model checkpoints of the reward model."}
+    )
+    reward_model_quantization_bit: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of bits to quantize the reward model."}
+    )
+    reward_model_type: Optional[Literal["lora", "full"]] = field(
+        default="lora",
+        metadata={"help": "The checkpoint type of the reward model. The lora type only supports lora training."}
+    )
+
+
+@dataclass
+class FinetuningArguments(FreezeArguments, LoraArguments, RLHFArguments):
+    r"""
+    Arguments pertaining to which techniques we are going to fine-tuning with.
+    """
+    stage: Optional[Literal["pt", "sft", "rm", "ppo", "dpo"]] = field(
+        default="sft",
+        metadata={"help": "Which stage will be performed in training."}
+    )
+    finetuning_type: Optional[Literal["lora", "freeze", "full"]] = field(
+        default="lora",
+        metadata={"help": "Which fine-tuning method to use."}
+    )
     upcast_layernorm: Optional[bool] = field(
         default=False,
         metadata={"help": "Whether to upcast the layernorm weights in fp32."}
@@ -91,15 +137,37 @@ class FinetuningArguments:
         default=0,
         metadata={"help": "The alpha parameter to control the noise magnitude in NEFTune."}
     )
+    export_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Path to the directory to save the exported model."}
+    )
+    plot_loss: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
+    )
 
     def __post_init__(self):
-        if isinstance(self.lora_target, str): # support custom target modules/layers of LoRA
+        def split_arg(arg):
-            self.lora_target = [target.strip() for target in self.lora_target.split(",")]
+            if isinstance(arg, str):
+                return [item.strip() for item in arg.split(",")]
+            return arg
 
-        if isinstance(self.additional_target, str):
+        self.name_module_trainable = split_arg(self.name_module_trainable)
-            self.additional_target = [target.strip() for target in self.additional_target.split(",")]
+        self.lora_alpha = self.lora_alpha or float(self.lora_rank * 2.0)
+        self.lora_target = split_arg(self.lora_target)
+        self.additional_target = split_arg(self.additional_target)
+        self.ref_model_checkpoint = split_arg(self.ref_model_checkpoint)
+        self.reward_model_checkpoint = split_arg(self.reward_model_checkpoint)
 
         assert self.finetuning_type in ["lora", "freeze", "full"], "Invalid fine-tuning method."
+        assert self.ref_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+        assert self.reward_model_quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
+
+        if self.stage == "ppo" and self.reward_model is None:
+            raise ValueError("Reward model is necessary for PPO training.")
+
+        if self.stage == "ppo" and self.reward_model_type == "lora" and self.finetuning_type != "lora":
+            raise ValueError("Lora reward model only supports lora training.")
 
     def save_to_json(self, json_path: str):
         r"""Saves the content of this instance in JSON format inside `json_path`."""
@@ -112,4 +180,5 @@ class FinetuningArguments:
         r"""Creates an instance from the content of `json_path`."""
         with open(json_path, "r", encoding="utf-8") as f:
             text = f.read()
+
         return cls(**json.loads(text))

src/llmtuner/hparams/model_args.py

@@ -54,22 +54,10 @@ class ModelArguments:
         default=False,
         metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."}
     )
-    reward_model: Optional[str] = field( # TODO: move it to FinetuningArguments
-        default=None,
-        metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
-    )
-    plot_loss: Optional[bool] = field( # TODO: move it to FinetuningArguments
-        default=False,
-        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
-    )
     hf_hub_token: Optional[str] = field(
         default=None,
         metadata={"help": "Auth token to log in with Hugging Face Hub."}
     )
-    export_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Path to the directory to save the exported model."}
-    )
 
     def __post_init__(self):
         self.compute_dtype = None
@@ -81,8 +69,7 @@ class ModelArguments:
         if self.checkpoint_dir is not None: # support merging multiple lora weights
             self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
 
-        if self.quantization_bit is not None:
+        assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization."
-            assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
 
     def to_dict(self) -> Dict[str, Any]:
         return asdict(self)

src/llmtuner/model/__init__.py

@@ -0,0 +1,5 @@
+# Level: loader > adapter > parser, utils
+
+from llmtuner.model.loader import load_model_and_tokenizer
+from llmtuner.model.parser import get_train_args, get_infer_args, get_eval_args
+from llmtuner.model.utils import dispatch_model, generate_model_card, load_valuehead_params

src/llmtuner/model/adapter.py

@@ -1,18 +1,9 @@
-import os
 import torch
 from typing import TYPE_CHECKING
-
+from peft import PeftModel, TaskType, LoraConfig, get_peft_model
-from transformers.utils import cached_file
-from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
-from peft import (
-    PeftModel,
-    TaskType,
-    LoraConfig,
-    get_peft_model
-)
 
 from llmtuner.extras.logging import get_logger
-from llmtuner.tuner.core.utils import find_all_linear_modules
+from llmtuner.model.utils import find_all_linear_modules
 
 if TYPE_CHECKING:
     from transformers.modeling_utils import PreTrainedModel
@@ -46,13 +37,23 @@ def init_adapter(
 
     if finetuning_args.finetuning_type == "freeze" and is_trainable:
         logger.info("Fine-tuning method: Freeze")
-        num_layers = getattr(model.config, "num_layers")
+        num_layers = (
+            getattr(model.config, "num_hidden_layers", None)
+            or getattr(model.config, "num_layers", None)
+            or getattr(model.config, "n_layer", None)
+        )
+        if not num_layers:
+            raise ValueError("Current model does not support freeze tuning.")
         if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
             trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
         else: # fine-tuning the first n layers if num_layer_trainable < 0
             trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]
 
-        trainable_layers = ["{:d}.{}".format(idx, finetuning_args.name_module_trainable) for idx in trainable_layer_ids]
+        trainable_layers = []
+        for module_name in finetuning_args.name_module_trainable:
+            for idx in trainable_layer_ids:
+                trainable_layers.append("{:d}.{}".format(idx, module_name))
+
         for name, param in model.named_parameters():
             if not any(trainable_layer in name for trainable_layer in trainable_layers):
                 param.requires_grad_(False)
@@ -100,30 +101,3 @@ def init_adapter(
         logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
 
     return model
-
-
-def load_valuehead_params(
-    model: "PreTrainedModel",
-    model_args: "ModelArguments"
-) -> bool:
-    kwargs = {
-        "path_or_repo_id": model_args.reward_model,
-        "cache_dir": model_args.cache_dir,
-        "token": model_args.hf_hub_token,
-        "revision": model_args.model_revision
-    }
-    try:
-        vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
-    except:
-        try:
-            vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
-        except:
-            logger.warning("Provided path ({}) does not contain valuehead weights.".format(model_args.reward_model))
-            return False
-
-    vhead_params = torch.load(vhead_file, map_location="cpu")
-    model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
-    model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
-    model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
-    model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
-    return True

src/llmtuner/model/loader.py

@@ -15,7 +15,6 @@ from transformers import (
 )
 from transformers.models.llama import modeling_llama as LlamaModule
 from transformers.utils.versions import require_version
-from peft import PeftModel
 from trl import AutoModelForCausalLMWithValueHead
 
 try:
@@ -24,11 +23,12 @@ except ImportError: # https://github.com/huggingface/transformers/releases/tag/v
     from transformers.deepspeed import is_deepspeed_zero3_enabled
 
 from llmtuner.extras.logging import reset_logging, get_logger
-from llmtuner.extras.misc import count_parameters, infer_optim_dtype
+from llmtuner.extras.misc import count_parameters, get_current_device, infer_optim_dtype
+from llmtuner.extras.packages import is_flash_attn2_available
 from llmtuner.extras.patches import llama_patch as LlamaPatches
 from llmtuner.hparams import FinetuningArguments
-from llmtuner.tuner.core.adapter import init_adapter, load_valuehead_params
+from llmtuner.model.adapter import init_adapter
-from llmtuner.tuner.core.utils import prepare_model_for_training
+from llmtuner.model.utils import load_valuehead_params, prepare_model_for_training
 
 if TYPE_CHECKING:
     from transformers import PreTrainedTokenizer
@@ -73,6 +73,7 @@ def load_model_and_tokenizer(
     )
 
     if finetuning_args.finetuning_type != "lora" and model_args.checkpoint_dir is not None:
+        logger.info("Use `model_name_or_path` to specify the model trained with full/freeze method.")
         model_to_load = model_args.checkpoint_dir[0]
     else:
         model_to_load = model_args.model_name_or_path
@@ -122,7 +123,7 @@ def load_model_and_tokenizer(
     # Set FlashAttention-2
     if model_args.flash_attn:
         if getattr(config, "model_type", None) == "llama":
-            if LlamaPatches.is_flash_attn_2_available:
+            if is_flash_attn2_available():
                 LlamaModule.LlamaAttention = LlamaPatches.LlamaFlashAttention2
                 LlamaModule.LlamaModel._prepare_decoder_attention_mask = LlamaPatches._prepare_decoder_attention_mask
                 logger.info("Using FlashAttention-2 for faster training and inference.")
@@ -131,7 +132,7 @@ def load_model_and_tokenizer(
         elif getattr(config, "model_type", None) in ["qwen", "Yi"]:
             logger.info("Current model automatically enables FlashAttention if installed.")
         else:
-            logger.warning("Current model does not support FlashAttention-2.")
+            logger.warning("Current model does not support FlashAttention.")
     elif is_trainable and model_args.shift_attn and getattr(config, "model_type", None) == "llama":
         LlamaModule.LlamaAttention = LlamaPatches.LlamaShiftShortAttention
         logger.warning("Using `--flash_attn` for faster training in large context length.")
@@ -144,7 +145,7 @@ def load_model_and_tokenizer(
         else:
             logger.warning("Current model does not support shift short attention.")
 
-    # Quantization configurations (using bitsandbytes library).
+    # Quantization configurations (using bitsandbytes library)
     if model_args.quantization_bit is not None:
         if is_deepspeed_zero3_enabled():
             raise ValueError("DeepSpeed ZeRO-3 is incompatible with quantization.")
@@ -164,10 +165,10 @@ def load_model_and_tokenizer(
                 bnb_4bit_quant_type=model_args.quantization_type
             )
 
-        config_kwargs["device_map"] = {"": int(os.environ.get("LOCAL_RANK", "0"))} if is_trainable else "auto"
+        config_kwargs["device_map"] = {"": get_current_device()}
         logger.info("Quantizing model to {} bit.".format(model_args.quantization_bit))
 
-    # Load and prepare pre-trained models (without valuehead).
+    # Load pre-trained models (without valuehead)
     model = AutoModelForCausalLM.from_pretrained(
         model_to_load,
         config=config,
@@ -185,7 +186,7 @@ def load_model_and_tokenizer(
         setattr(model, "lm_head", model.transformer.output_layer)
         setattr(model, "_keys_to_ignore_on_save", ["lm_head.weight"])
 
-    # Register auto class to save the custom code files.
+    # Register auto class to save the custom code files
     if isinstance(config, PretrainedConfig) and "AutoConfig" in getattr(config, "auto_map", {}):
         config.__class__.register_for_auto_class()
     if isinstance(model, PreTrainedModel) and "AutoModelForCausalLM" in getattr(config, "auto_map", {}):
@@ -199,25 +200,15 @@ def load_model_and_tokenizer(
     model = model.train() if is_trainable else model.eval()
 
     # Prepare model with valuehead for RLHF
-    if stage == "rm" or stage == "ppo":
+    if stage in ["rm", "ppo"]:
         model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(model)
-        reset_logging()
+        vhead_path = (
-        if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
+            model_args.checkpoint_dir[-1] if model_args.checkpoint_dir is not None else model_args.model_name_or_path
-            logger.warning("Only the last checkpoint containing valuehead will be loaded.")
+        )
-            if load_valuehead_params(model, model_args):
+        vhead_params = load_valuehead_params(vhead_path, model_args)
-                model.v_head.load_state_dict({
+        if vhead_params is not None:
-                    "summary.weight": getattr(model, "reward_head_weight"),
+            model.load_state_dict(vhead_params, strict=False)
-                    "summary.bias": getattr(model, "reward_head_bias")
+            logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
-                })
-
-        if stage == "ppo": # load reward model
-            logger.info("Load reward model from {}".format(model_args.reward_model))
-            if isinstance(model.pretrained_model, PeftModel):
-                model.pretrained_model.load_adapter(model_args.reward_model, "reward")
-            for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
-                if "default" in name:
-                    param.data = param.data.to(torch.float32) # trainable params should in fp32
-            assert load_valuehead_params(model, model_args), "Reward model is not correctly loaded."
 
     # Prepare model for inference
     if not is_trainable:

src/llmtuner/model/parser.py

@@ -11,6 +11,7 @@ from llmtuner.extras.misc import parse_args
 from llmtuner.hparams import (
     ModelArguments,
     DataArguments,
+    EvaluationArguments,
     FinetuningArguments,
     GeneratingArguments
 )
@@ -19,51 +20,42 @@ from llmtuner.hparams import (
 logger = get_logger(__name__)
 
 
-def parse_train_args(
+_TRAIN_ARGS = [
-    args: Optional[Dict[str, Any]] = None
+    ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
-) -> Tuple[
+]
-    ModelArguments,
+_TRAIN_CLS = Tuple[
-    DataArguments,
+    ModelArguments, DataArguments, Seq2SeqTrainingArguments, FinetuningArguments, GeneratingArguments
-    Seq2SeqTrainingArguments,
+]
-    FinetuningArguments,
+_INFER_ARGS = [
-    GeneratingArguments
+    ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
-]:
+]
-    parser = HfArgumentParser((
+_INFER_CLS = Tuple[
-        ModelArguments,
+    ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
-        DataArguments,
+]
-        Seq2SeqTrainingArguments,
+_EVAL_ARGS = [
-        FinetuningArguments,
+    ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
-        GeneratingArguments
+]
-    ))
+_EVAL_CLS = Tuple[
+    ModelArguments, DataArguments, EvaluationArguments, FinetuningArguments
+]
+
+
+def parse_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
+    parser = HfArgumentParser(_TRAIN_ARGS)
     return parse_args(parser, args)
 
 
-def parse_infer_args(
+def parse_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
-    args: Optional[Dict[str, Any]] = None
+    parser = HfArgumentParser(_INFER_ARGS)
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments
-]:
-    parser = HfArgumentParser((
-        ModelArguments,
-        DataArguments,
-        FinetuningArguments,
-        GeneratingArguments
-    ))
     return parse_args(parser, args)
 
 
-def get_train_args(
+def parse_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
-    args: Optional[Dict[str, Any]] = None
+    parser = HfArgumentParser(_EVAL_ARGS)
-) -> Tuple[
+    return parse_args(parser, args)
-    ModelArguments,
+
-    DataArguments,
+
-    Seq2SeqTrainingArguments,
+def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
-    FinetuningArguments,
-    GeneratingArguments
-]:
     model_args, data_args, training_args, finetuning_args, generating_args = parse_train_args(args)
 
     # Setup logging
@@ -90,24 +82,19 @@ def get_train_args(
         raise ValueError("Please enable `predict_with_generate` to save model predictions.")
 
     if finetuning_args.stage in ["rm", "ppo"]:
-        if finetuning_args.finetuning_type != "lora":
-            raise ValueError("RM and PPO stages can only be performed with the LoRA method.")
         if training_args.resume_from_checkpoint is not None:
             raise ValueError("RM and PPO stages do not support `resume_from_checkpoint`.")
         if training_args.load_best_model_at_end:
             raise ValueError("RM and PPO stages do not support `load_best_model_at_end`.")
 
     if finetuning_args.stage == "ppo" and not training_args.do_train:
-        raise ValueError("PPO training does not support evaluation.")
+        raise ValueError("PPO training does not support evaluation, use the SFT stage to evaluate models.")
 
     if finetuning_args.stage in ["rm", "dpo"]:
         for dataset_attr in data_args.dataset_list:
             if not dataset_attr.ranking:
                 raise ValueError("Please use ranked datasets for reward modeling or DPO training.")
 
-    if finetuning_args.stage == "ppo" and model_args.reward_model is None:
-        raise ValueError("Reward model is necessary for PPO training.")
-
     if finetuning_args.stage == "ppo" and model_args.shift_attn:
         raise ValueError("PPO training is incompatible with S^2-Attn.")
 
@@ -139,6 +126,9 @@ def get_train_args(
     if (not training_args.do_train) and model_args.quantization_bit is not None:
         logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
 
+    if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
+        logger.warning("Specify `ref_model` for computing rewards at evaluation.")
+
     # postprocess training_args
     if (
         training_args.local_rank != -1
@@ -187,14 +177,7 @@ def get_train_args(
     return model_args, data_args, training_args, finetuning_args, generating_args
 
 
-def get_infer_args(
+def get_infer_args(args: Optional[Dict[str, Any]] = None) -> _INFER_CLS:
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    FinetuningArguments,
-    GeneratingArguments
-]:
     model_args, data_args, finetuning_args, generating_args = parse_infer_args(args)
 
     if data_args.template is None:
@@ -211,3 +194,17 @@ def get_infer_args(
         raise ValueError("Only LoRA tuning accepts multiple checkpoints.")
 
     return model_args, data_args, finetuning_args, generating_args
+
+
+def get_eval_args(args: Optional[Dict[str, Any]] = None) -> _EVAL_CLS:
+    model_args, data_args, eval_args, finetuning_args = parse_eval_args(args)
+
+    if data_args.template is None:
+        raise ValueError("Please specify which `template` to use.")
+
+    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
+        raise ValueError("Quantization is only compatible with the LoRA method.")
+
+    transformers.set_seed(eval_args.seed)
+
+    return model_args, data_args, eval_args, finetuning_args

src/llmtuner/model/utils.py

@@ -1,21 +1,53 @@
 import torch
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
 
+from transformers.utils import cached_file
+from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
+
 from llmtuner.extras.constants import LAYERNORM_NAMES
 from llmtuner.extras.logging import get_logger
+from llmtuner.hparams import ModelArguments, FinetuningArguments
 
 if TYPE_CHECKING:
     from transformers.modeling_utils import PreTrainedModel
-    from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
+    from llmtuner.hparams import DataArguments
 
 
 logger = get_logger(__name__)
 
 
+def dispatch_model(model: "PreTrainedModel") -> "PreTrainedModel":
+    r"""
+    Dispatches a pre-trained model to GPUs with balanced memory.
+    Borrowed from: https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/modeling_utils.py#L2803
+    """
+    if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False): # do nothing
+        return model
+
+    if torch.cuda.device_count() > 1:
+        from accelerate import dispatch_model
+        from accelerate.utils import infer_auto_device_map, get_balanced_memory
+
+        if model._no_split_modules is None:
+            raise ValueError("The model class needs to implement the `_no_split_modules` attribute.")
+
+        kwargs = {"dtype": model.dtype, "no_split_module_classes": model._no_split_modules}
+        max_memory = get_balanced_memory(model, **kwargs)
+        # Make sure tied weights are tied before creating the device map.
+        model.tie_weights()
+        device_map = infer_auto_device_map(model, max_memory=max_memory, **kwargs)
+        return dispatch_model(model, device_map)
+    else:
+        return model.cuda()
+
+
 def find_all_linear_modules(
     model: "PreTrainedModel",
     quantization_bit: Optional[int] = None
 ) -> List[str]:
+    r"""
+    Finds all available modules to apply lora.
+    """
     if quantization_bit is not None:
         import bitsandbytes as bnb
         linear_cls = bnb.nn.Linear4bit if quantization_bit == 4 else bnb.nn.Linear8bitLt
@@ -51,6 +83,32 @@ def generate_model_card(
     }
 
 
+def load_valuehead_params(
+    path_or_repo_id: str,
+    model_args: "ModelArguments"
+) -> Dict[str, torch.Tensor]:
+    r"""
+    Loads value head parameters from Hugging Face Hub or local disk.
+
+    Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
+    """
+    kwargs = {
+        "path_or_repo_id": path_or_repo_id,
+        "cache_dir": model_args.cache_dir,
+        "token": model_args.hf_hub_token
+    }
+    try:
+        vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
+    except:
+        try:
+            vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
+        except:
+            logger.warning("Provided path ({}) does not contain valuehead weights.".format(path_or_repo_id))
+            return None
+
+    return torch.load(vhead_file, map_location="cpu")
+
+
 def prepare_model_for_training(
     model: "PreTrainedModel",
     finetuning_args: "FinetuningArguments",

src/llmtuner/train/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.tuner import export_model, run_exp

src/llmtuner/train/dpo/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.dpo.workflow import run_dpo

src/llmtuner/train/dpo/trainer.py

@@ -43,6 +43,10 @@ class CustomDPOTrainer(DPOTrainer):
 
         if ref_model is not None:
             if self.is_deepspeed_enabled:
+                if not (
+                    getattr(ref_model, "is_loaded_in_8bit", False)
+                    or getattr(ref_model, "is_loaded_in_4bit", False)
+                ): # quantized models are already set on the correct device
                     self.ref_model = self._prepare_deepspeed(self.ref_model)
             else:
                 self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

src/llmtuner/train/dpo/workflow.py

@@ -4,23 +4,20 @@ from peft import PeftModel
 from typing import TYPE_CHECKING, Optional, List
 from transformers import Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.logging import get_logger
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.hparams import ModelArguments
-from llmtuner.tuner.core import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
-from llmtuner.tuner.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.train.utils import create_ref_model
-from llmtuner.tuner.dpo.trainer import CustomDPOTrainer
+from llmtuner.train.dpo.collator import DPODataCollatorWithPadding
+from llmtuner.train.dpo.trainer import CustomDPOTrainer
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
     from llmtuner.hparams import DataArguments, FinetuningArguments
 
 
-logger = get_logger(__name__)
-
-
 def run_dpo(
     model_args: "ModelArguments",
     data_args: "DataArguments",
@@ -38,23 +35,10 @@ def run_dpo(
     )
 
     # Create reference model
-    if finetuning_args.dpo_ref_model is not None:
+    if finetuning_args.ref_model is None and (not training_args.do_train): # use the model itself
-        ref_model_args_dict = model_args.to_dict()
-        ref_model_args_dict.update(dict(
-            model_name_or_path=finetuning_args.dpo_ref_model,
-            checkpoint_dir=finetuning_args.dpo_ref_model_checkpoint
-        ))
-        ref_model_args = ModelArguments(**ref_model_args_dict)
-        ref_model, _ = load_model_and_tokenizer(ref_model_args, finetuning_args, is_trainable=False, stage="sft")
-        logger.info("Created reference model from {}".format(finetuning_args.dpo_ref_model))
-    elif training_args.do_train:
-        if isinstance(model, PeftModel):
-            ref_model = None
-        else:
-            ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage="sft")
-            logger.info("Created reference model from the model itself.")
-    else:
         ref_model = model
+    else:
+        ref_model = create_ref_model(model_args, finetuning_args, stage="dpo")
 
     # Update arguments
     training_args_dict = training_args.to_dict()
@@ -80,14 +64,13 @@ def run_dpo(
         trainer.log_metrics("train", train_result.metrics)
         trainer.save_metrics("train", train_result.metrics)
         trainer.save_state()
-        if trainer.is_world_process_zero() and model_args.plot_loss:
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
 
     # Evaluation
     if training_args.do_eval:
         metrics = trainer.evaluate(metric_key_prefix="eval")
         if id(model) == id(ref_model): # unable to compute rewards without a reference model
-            logger.warning("Pass `dpo_ref_model` for computing rewards at evaluation.")
             remove_keys = [key for key in metrics.keys() if "rewards" in key]
             for key in remove_keys:
                 metrics.pop(key)

src/llmtuner/train/ppo/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.ppo.workflow import run_ppo

src/llmtuner/train/ppo/trainer.py

@@ -3,7 +3,7 @@ import sys
 import math
 import torch
 from tqdm import tqdm
-from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, List, Optional, Tuple
 
 from transformers import BatchEncoding, GenerationConfig, Trainer, TrainerState, TrainerControl
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
@@ -14,7 +14,7 @@ from trl.core import PPODecorators, logprobs_from_logits
 from llmtuner.extras.callbacks import LogCallback, SavePeftModelCallback
 from llmtuner.extras.logging import get_logger
 from llmtuner.extras.misc import AverageMeter, count_parameters, get_logits_processor
-from llmtuner.tuner.ppo.utils import dump_layernorm, restore_layernorm, replace_model
+from llmtuner.train.ppo.utils import dump_layernorm, restore_layernorm, replace_model
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -37,24 +37,43 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         finetuning_args: "FinetuningArguments",
         generating_args: "GeneratingArguments",
         callbacks: List["TrainerCallback"],
+        reward_model: "AutoModelForCausalLMWithValueHead",
         **kwargs
     ):
         PPOTrainer.__init__(self, **kwargs)
+
         self.args = training_args
         self.model_args = model_args
         self.finetuning_args = finetuning_args
+        self.reward_model = reward_model
+
         self.generation_config = GenerationConfig(
             pad_token_id=self.tokenizer.pad_token_id,
             eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
             **generating_args.to_dict()
         )
+
         self.state = TrainerState()
         self.control = TrainerControl()
         self.log_callback, self.save_callback = callbacks[0], callbacks[1]
         assert isinstance(self.log_callback, LogCallback) and isinstance(self.save_callback, SavePeftModelCallback)
+
         if self.args.max_steps > 0:
             logger.info("max_steps is given, it will override any value given in num_train_epochs")
 
+        if reward_model is not None:
+            is_deepspeed_enabled = self.accelerator.distributed_type == "DEEPSPEED" and hasattr(
+                self.accelerator.state, "deepspeed_plugin"
+            )
+            if is_deepspeed_enabled:
+                if not (
+                    getattr(reward_model.pretrained_model, "is_loaded_in_8bit", False)
+                    or getattr(reward_model.pretrained_model, "is_loaded_in_4bit", False)
+                ): # quantized models are already set on the correct device
+                    self.reward_model = self._prepare_deepspeed(self.reward_model)
+            else:
+                self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True)
+
     def ppo_train(self) -> None:
         r"""
         Implements training loop for the PPO stage, like _inner_training_loop() in Huggingface's Trainer.
@@ -213,11 +232,14 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
         r"""
         Computes scores using given reward model.
         """
+        if self.reward_model is None:
             replace_model(unwrapped_model, target="reward")
+
         batch = self.prepare_model_inputs(queries, responses)
 
         with torch.cuda.amp.autocast(dtype=self.model_args.compute_dtype): # support bf16
-            _, _, values = self.model(**batch, output_hidden_states=True, return_dict=True)
+            reward_model = self.reward_model if self.reward_model is not None else self.model
+            _, _, values = reward_model(**batch, output_hidden_states=True, return_dict=True)
 
         if values.size(0) != batch["input_ids"].size(0): # adapt to chatglm2
             values = torch.transpose(values, 0, 1)
@@ -228,7 +250,9 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
             end_index = end_indexes[-1].item() if len(end_indexes) else 0
             rewards.append(values[i, end_index].float().detach().cpu()) # use fp32 type
 
+        if self.reward_model is None:
             replace_model(unwrapped_model, target="default")
+
         return rewards
 
     @PPODecorators.empty_device_cache()

src/llmtuner/train/ppo/workflow.py

@@ -7,11 +7,12 @@ from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorWithPadding
 from transformers.optimization import get_scheduler
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.data import get_dataset, preprocess_dataset
 from llmtuner.extras.callbacks import SavePeftModelCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import load_model_and_tokenizer
+from llmtuner.model import load_model_and_tokenizer
-from llmtuner.tuner.ppo.trainer import CustomPPOTrainer
+from llmtuner.train.utils import create_ref_model, create_reward_model
+from llmtuner.train.ppo.trainer import CustomPPOTrainer
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -33,6 +34,11 @@ def run_ppo(
     tokenizer.padding_side = "left" # use left-padding in generation while using right-padding in training
     data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
 
+    # Create reference model and reward model
+    ref_model = create_ref_model(model_args, finetuning_args, stage="ppo")
+    reward_model = create_reward_model(model, model_args, finetuning_args)
+
+    # Create ppo config
     ppo_config = PPOConfig(
         model_name=model_args.model_name_or_path,
         learning_rate=training_args.learning_rate,
@@ -47,9 +53,11 @@ def run_ppo(
         log_with=finetuning_args.ppo_logger,
         use_score_scaling=finetuning_args.ppo_score_norm,
         use_score_norm=finetuning_args.ppo_score_norm,
+        whiten_rewards=finetuning_args.ppo_whiten_rewards,
         accelerator_kwargs={"step_scheduler_with_optimizer": False}
     )
 
+    # Create optimizer and scheduler
     optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
     if training_args.max_steps > 0:
         num_training_steps = training_args.max_steps
@@ -73,9 +81,10 @@ def run_ppo(
         finetuning_args=finetuning_args,
         generating_args=generating_args,
         callbacks=callbacks + [SavePeftModelCallback()],
+        reward_model=reward_model,
         config=ppo_config,
         model=model,
-        ref_model=None,
+        ref_model=ref_model,
         tokenizer=tokenizer,
         dataset=dataset,
         data_collator=data_collator,
@@ -88,5 +97,5 @@ def run_ppo(
         ppo_trainer.ppo_train()
         ppo_trainer.save_model()
         ppo_trainer.save_state() # must be called after save_model to have a folder
-        if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
+        if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "reward"])

src/llmtuner/train/pt/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.pt.workflow import run_pt

src/llmtuner/train/pt/workflow.py

@@ -4,9 +4,9 @@ import math
 from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForLanguageModeling, Trainer
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
 
 if TYPE_CHECKING:
     from transformers import Seq2SeqTrainingArguments, TrainerCallback
@@ -42,7 +42,7 @@ def run_pt(
         trainer.log_metrics("train", train_result.metrics)
         trainer.save_metrics("train", train_result.metrics)
         trainer.save_state()
-        if trainer.is_world_process_zero() and model_args.plot_loss:
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
 
     # Evaluation

src/llmtuner/train/rm/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.rm.workflow import run_rm

src/llmtuner/train/rm/workflow.py

@@ -3,13 +3,13 @@
 from typing import TYPE_CHECKING, Optional, List
 from transformers import Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.callbacks import SavePeftModelCallback
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
-from llmtuner.tuner.rm.metric import compute_accuracy
+from llmtuner.train.rm.collator import PairwiseDataCollatorWithPadding
-from llmtuner.tuner.rm.collator import PairwiseDataCollatorWithPadding
+from llmtuner.train.rm.metric import compute_accuracy
-from llmtuner.tuner.rm.trainer import PairwiseTrainer
+from llmtuner.train.rm.trainer import PairwiseTrainer
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -51,7 +51,7 @@ def run_rm(
         trainer.log_metrics("train", train_result.metrics)
         trainer.save_metrics("train", train_result.metrics)
         trainer.save_state()
-        if trainer.is_world_process_zero() and model_args.plot_loss:
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
 
     # Evaluation

src/llmtuner/train/sft/__init__.py

@@ -0,0 +1 @@
+from llmtuner.train.sft.workflow import run_sft

src/llmtuner/train/sft/metric.py

@@ -2,15 +2,23 @@ import numpy as np
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, Sequence, Tuple, Union
 
-import jieba
-from rouge_chinese import Rouge
-from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
-
 from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.packages import (
+    is_jieba_available, is_nltk_available, is_rouge_available
+)
 
 if TYPE_CHECKING:
     from transformers.tokenization_utils import PreTrainedTokenizer
 
+if is_jieba_available():
+    import jieba
+
+if is_nltk_available():
+    from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+
+if is_rouge_available():
+    from rouge_chinese import Rouge
+
 
 @dataclass
 class ComputeMetrics:

src/llmtuner/train/sft/workflow.py

@@ -3,13 +3,13 @@
 from typing import TYPE_CHECKING, Optional, List
 from transformers import DataCollatorForSeq2Seq, Seq2SeqTrainingArguments
 
-from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
+from llmtuner.data import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.misc import get_logits_processor
 from llmtuner.extras.ploting import plot_loss
-from llmtuner.tuner.core import generate_model_card, load_model_and_tokenizer
+from llmtuner.model import generate_model_card, load_model_and_tokenizer
-from llmtuner.tuner.sft.metric import ComputeMetrics
+from llmtuner.train.sft.metric import ComputeMetrics
-from llmtuner.tuner.sft.trainer import CustomSeq2SeqTrainer
+from llmtuner.train.sft.trainer import CustomSeq2SeqTrainer
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -69,7 +69,7 @@ def run_sft(
         trainer.log_metrics("train", train_result.metrics)
         trainer.save_metrics("train", train_result.metrics)
         trainer.save_state()
-        if trainer.is_world_process_zero() and model_args.plot_loss:
+        if trainer.is_world_process_zero() and finetuning_args.plot_loss:
             plot_loss(training_args.output_dir, keys=["loss", "eval_loss"])
 
     # Evaluation

src/llmtuner/train/tuner.py

@@ -2,12 +2,12 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional
 
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.logging import get_logger
-from llmtuner.tuner.core import get_train_args, get_infer_args, load_model_and_tokenizer
+from llmtuner.model import get_train_args, get_infer_args, load_model_and_tokenizer
-from llmtuner.tuner.pt import run_pt
+from llmtuner.train.pt import run_pt
-from llmtuner.tuner.sft import run_sft
+from llmtuner.train.sft import run_sft
-from llmtuner.tuner.rm import run_rm
+from llmtuner.train.rm import run_rm
-from llmtuner.tuner.ppo import run_ppo
+from llmtuner.train.ppo import run_ppo
-from llmtuner.tuner.dpo import run_dpo
+from llmtuner.train.dpo import run_dpo
 
 if TYPE_CHECKING:
     from transformers import TrainerCallback
@@ -38,11 +38,11 @@ def export_model(args: Optional[Dict[str, Any]] = None, max_shard_size: Optional
     model_args, _, finetuning_args, _ = get_infer_args(args)
     model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
     model.config.use_cache = True
-    model.save_pretrained(model_args.export_dir, max_shard_size=max_shard_size)
+    model.save_pretrained(finetuning_args.export_dir, max_shard_size=max_shard_size)
     try:
         tokenizer.padding_side = "left" # restore padding side
         tokenizer.init_kwargs["padding_side"] = "left"
-        tokenizer.save_pretrained(model_args.export_dir)
+        tokenizer.save_pretrained(finetuning_args.export_dir)
     except:
         logger.warning("Cannot save tokenizer, please copy the files manually.")
 

src/llmtuner/train/utils.py

@@ -0,0 +1,80 @@
+import torch
+from typing import TYPE_CHECKING, Literal, Union
+
+from llmtuner.extras.logging import get_logger
+from llmtuner.hparams import ModelArguments, FinetuningArguments
+from llmtuner.model import load_model_and_tokenizer, load_valuehead_params
+
+if TYPE_CHECKING:
+    from transformers.modeling_utils import PreTrainedModel
+    from trl import AutoModelForCausalLMWithValueHead
+
+
+logger = get_logger(__name__)
+
+
+def create_ref_model(
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments",
+    stage: Literal["ppo", "dpo"]
+) -> Union["PreTrainedModel", "AutoModelForCausalLMWithValueHead"]:
+    r"""
+    Creates reference model for PPO/DPO training. Evaluation mode is not supported.
+
+    The valuehead parameter is randomly initialized since it is useless for PPO training.
+    """
+    if finetuning_args.ref_model is not None:
+        ref_model_args_dict = model_args.to_dict()
+        ref_model_args_dict.update(dict(
+            model_name_or_path=finetuning_args.ref_model,
+            checkpoint_dir=finetuning_args.ref_model_checkpoint,
+            quantization_bit=finetuning_args.ref_model_quantization_bit
+        ))
+        ref_model_args = ModelArguments(**ref_model_args_dict)
+        ref_finetuning_args = FinetuningArguments(finetuning_type="lora")
+        ref_model, _ = load_model_and_tokenizer(ref_model_args, ref_finetuning_args, is_trainable=False, stage=stage)
+        logger.info("Created reference model from {}".format(finetuning_args.ref_model))
+    else:
+        if finetuning_args.finetuning_type == "lora":
+            ref_model = None
+        else:
+            ref_model, _ = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, stage=stage)
+            logger.info("Created reference model from the model itself.")
+
+    return ref_model
+
+
+def create_reward_model(
+    model: "AutoModelForCausalLMWithValueHead",
+    model_args: "ModelArguments",
+    finetuning_args: "FinetuningArguments"
+) -> "AutoModelForCausalLMWithValueHead":
+    r"""
+    Creates reward model for PPO training.
+    """
+    if finetuning_args.reward_model_type == "lora":
+        model.pretrained_model.load_adapter(finetuning_args.reward_model, "reward")
+        for name, param in model.named_parameters(): # https://github.com/huggingface/peft/issues/1090
+            if "default" in name:
+                param.data = param.data.to(torch.float32) # trainable params should in fp32
+        vhead_params = load_valuehead_params(finetuning_args.reward_model, model_args)
+        assert vhead_params is not None, "Reward model is not correctly loaded."
+        model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
+        model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
+        model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
+        model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
+        logger.info("Loaded adapter weights of reward model from {}".format(finetuning_args.reward_model))
+        return None
+    else:
+        reward_model_args_dict = model_args.to_dict()
+        reward_model_args_dict.update(dict(
+            model_name_or_path=finetuning_args.reward_model,
+            checkpoint_dir=finetuning_args.reward_model_checkpoint,
+            quantization_bit=finetuning_args.reward_model_quantization_bit
+        ))
+        reward_model_args = ModelArguments(**reward_model_args_dict)
+        reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
+        reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
+        logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
+        logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
+        return reward_model

src/llmtuner/tuner/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.tune import export_model, run_exp

src/llmtuner/tuner/core/__init__.py

@@ -1,3 +0,0 @@
-from llmtuner.tuner.core.parser import get_train_args, get_infer_args
-from llmtuner.tuner.core.loader import load_model_and_tokenizer
-from llmtuner.tuner.core.utils import generate_model_card

src/llmtuner/tuner/dpo/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.dpo.workflow import run_dpo

src/llmtuner/tuner/ppo/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.ppo.workflow import run_ppo

src/llmtuner/tuner/pt/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.pt.workflow import run_pt

src/llmtuner/tuner/rm/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.rm.workflow import run_rm

src/llmtuner/tuner/sft/__init__.py

@@ -1 +0,0 @@
-from llmtuner.tuner.sft.workflow import run_sft

src/llmtuner/webui/chatter.py

@@ -2,7 +2,7 @@ import gradio as gr
 from gradio.components import Component # cannot use TYPE_CHECKING here
 from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
 
-from llmtuner.chat.stream_chat import ChatModel
+from llmtuner.chat import ChatModel
 from llmtuner.extras.misc import torch_gc
 from llmtuner.hparams import GeneratingArguments
 from llmtuner.webui.common import get_save_dir
@@ -14,14 +14,24 @@ if TYPE_CHECKING:
 
 class WebChatModel(ChatModel):
 
-    def __init__(self, manager: "Manager", lazy_init: Optional[bool] = True) -> None:
+    def __init__(
+        self,
+        manager: "Manager",
+        demo_mode: Optional[bool] = False,
+        lazy_init: Optional[bool] = True
+    ) -> None:
         self.manager = manager
+        self.demo_mode = demo_mode
         self.model = None
         self.tokenizer = None
         self.generating_args = GeneratingArguments()
-        if not lazy_init:
+
+        if not lazy_init: # read arguments from command line
             super().__init__()
 
+        if demo_mode: # load openchat 3.5 by default
+            super().__init__(dict(model_name_or_path="openchat/openchat_3.5", template="openchat"))
+
     @property
     def loaded(self) -> bool:
         return self.model is not None
@@ -36,6 +46,8 @@ class WebChatModel(ChatModel):
             error = ALERTS["err_no_model"][lang]
         elif not get("top.model_path"):
             error = ALERTS["err_no_path"][lang]
+        elif self.demo_mode:
+            error = ALERTS["err_demo"][lang]
 
         if error:
             gr.Warning(error)
@@ -67,6 +79,11 @@ class WebChatModel(ChatModel):
 
     def unload_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
         lang = data[self.manager.get_elem_by_name("top.lang")]
+
+        if self.demo_mode:
+            yield ALERTS["err_demo"][lang]
+            return
+
         yield ALERTS["info_unloading"][lang]
         self.model = None
         self.tokenizer = None

src/llmtuner/webui/common.py

@@ -70,7 +70,7 @@ def get_module(model_name: str) -> str:
 
 
 def get_template(model_name: str) -> str:
-    if model_name.endswith("Chat") and get_prefix(model_name) in DEFAULT_TEMPLATE:
+    if model_name and model_name.endswith("Chat") and get_prefix(model_name) in DEFAULT_TEMPLATE:
         return DEFAULT_TEMPLATE[get_prefix(model_name)]
     return "default"
 

src/llmtuner/webui/components/export.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from typing import TYPE_CHECKING, Dict, Generator, List
 
-from llmtuner.tuner import export_model
+from llmtuner.train import export_model
 from llmtuner.webui.common import get_save_dir
 from llmtuner.webui.locales import ALERTS
 

src/llmtuner/webui/components/top.py

@@ -1,8 +1,8 @@
 import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
+from llmtuner.data.template import templates
 from llmtuner.extras.constants import METHODS, SUPPORTED_MODELS
-from llmtuner.extras.template import templates
 from llmtuner.webui.common import get_model_path, get_template, list_checkpoint, save_config
 from llmtuner.webui.utils import can_quantize
 

src/llmtuner/webui/css.py

@@ -1,4 +1,11 @@
 CSS = r"""
+.duplicate-button {
+  margin: auto !important;
+  color: white !important;
+  background: black !important;
+  border-radius: 100vh !important;
+}
+
 .modal-box {
   position: fixed !important;
   top: 50%;

src/llmtuner/webui/engine.py

@@ -12,11 +12,11 @@ from llmtuner.webui.utils import get_time
 
 class Engine:
 
-    def __init__(self, pure_chat: Optional[bool] = False) -> None:
+    def __init__(self, demo_mode: Optional[bool] = False, pure_chat: Optional[bool] = False) -> None:
         self.pure_chat = pure_chat
-        self.manager: "Manager" = Manager()
+        self.manager = Manager()
-        self.runner: "Runner" = Runner(self.manager)
+        self.runner = Runner(self.manager, demo_mode=demo_mode)
-        self.chatter: "WebChatModel" = WebChatModel(manager=self.manager, lazy_init=(not pure_chat))
+        self.chatter = WebChatModel(manager=self.manager, demo_mode=demo_mode, lazy_init=(not pure_chat))
 
     def _form_dict(self, resume_dict: Dict[str, Dict[str, Any]]):
         return {self.manager.get_elem_by_name(k): gr.update(**v) for k, v in resume_dict.items()}

src/llmtuner/webui/interface.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from typing import Optional
 from transformers.utils.versions import require_version
 
 from llmtuner.webui.components import (
@@ -17,22 +18,33 @@ from llmtuner.webui.engine import Engine
 require_version("gradio>=3.38.0,<4.0.0", "To fix: pip install \"gradio>=3.38.0,<4.0.0\"")
 
 
-def create_ui() -> gr.Blocks:
+def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
-    engine = Engine(pure_chat=False)
+    engine = Engine(demo_mode=demo_mode, pure_chat=False)
 
     with gr.Blocks(title="LLaMA Board", css=CSS) as demo:
+        if demo_mode:
+            gr.HTML(
+                "<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>"
+            )
+            gr.HTML(
+                "<h3><center>Visit <a href=\"https://github.com/hiyouga/LLaMA-Factory\" target=\"_blank\">"
+                "LLaMA Factory</a> for details.</center></h3>"
+            )
+            gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
+
         engine.manager.all_elems["top"] = create_top()
         lang: "gr.Dropdown" = engine.manager.get_elem_by_name("top.lang")
 
         with gr.Tab("Train"):
             engine.manager.all_elems["train"] = create_train_tab(engine)
 
-        with gr.Tab("Evaluate"):
+        with gr.Tab("Evaluate & Predict"):
             engine.manager.all_elems["eval"] = create_eval_tab(engine)
 
         with gr.Tab("Chat"):
             engine.manager.all_elems["infer"] = create_infer_tab(engine)
 
+        if not demo_mode:
             with gr.Tab("Export"):
                 engine.manager.all_elems["export"] = create_export_tab(engine)
 

src/llmtuner/webui/locales.py

@@ -659,6 +659,10 @@ ALERTS = {
         "en": "Failed.",
         "zh": "训练出错。"
     },
+    "err_demo": {
+        "en": "Training is unavailable in demo mode, duplicate the space to a private one first.",
+        "zh": "展示模式不支持训练，请先复制到私人空间。"
+    },
     "info_aborting": {
         "en": "Aborted, wait for terminating...",
         "zh": "训练中断，正在等待线程结束……"

src/llmtuner/webui/runner.py

@@ -4,7 +4,7 @@ import logging
 import gradio as gr
 from threading import Thread
 from gradio.components import Component # cannot use TYPE_CHECKING here
-from typing import TYPE_CHECKING, Any, Dict, Generator, List, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Generator, Optional, Tuple
 
 import transformers
 from transformers.trainer import TRAINING_ARGS_NAME
@@ -13,7 +13,7 @@ from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import TRAINING_STAGES
 from llmtuner.extras.logging import LoggerHandler
 from llmtuner.extras.misc import torch_gc
-from llmtuner.tuner import run_exp
+from llmtuner.train import run_exp
 from llmtuner.webui.common import get_module, get_save_dir, load_config
 from llmtuner.webui.locales import ALERTS
 from llmtuner.webui.utils import gen_cmd, get_eval_results, update_process_bar
@@ -24,13 +24,13 @@ if TYPE_CHECKING:
 
 class Runner:
 
-    def __init__(self, manager: "Manager") -> None:
+    def __init__(self, manager: "Manager", demo_mode: Optional[bool] = False) -> None:
         self.manager = manager
+        self.demo_mode = demo_mode
         """ Resume """
         self.thread: "Thread" = None
         self.do_train = True
         self.running_data: Dict["Component", Any] = None
-        self.monitor_inputs: Dict[str, str] = None
         """ State """
         self.aborted = False
         self.running = False
@@ -46,9 +46,8 @@ class Runner:
 
     def set_abort(self) -> None:
         self.aborted = True
-        self.running = False
 
-    def _initialize(self, data: Dict[Component, Any], do_train: bool) -> str:
+    def _initialize(self, data: Dict[Component, Any], do_train: bool, from_preview: bool) -> str:
         get = lambda name: data[self.manager.get_elem_by_name(name)]
         lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
         dataset = get("train.dataset") if do_train else get("eval.dataset")
@@ -65,6 +64,9 @@ class Runner:
         if len(dataset) == 0:
             return ALERTS["err_no_dataset"][lang]
 
+        if self.demo_mode and (not from_preview):
+            return ALERTS["err_demo"][lang]
+
         self.aborted = False
         self.logger_handler.reset()
         self.trainer_callback = LogCallback(self)
@@ -72,6 +74,7 @@ class Runner:
 
     def _finalize(self, lang: str, finish_info: str) -> str:
         self.thread = None
+        self.running_data = None
         self.running = False
         torch_gc()
         if self.aborted:
@@ -84,9 +87,9 @@ class Runner:
         user_config = load_config()
 
         if get("top.checkpoints"):
-            checkpoint_dir = ",".join([
+            checkpoint_dir = ",".join([get_save_dir(
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints")
+                get("top.model_name"), get("top.finetuning_type"), ckpt
-            ])
+            ) for ckpt in get("top.checkpoints")])
         else:
             checkpoint_dir = None
 
@@ -136,7 +139,10 @@ class Runner:
             args["upcast_layernorm"] = True
 
         if args["stage"] == "ppo":
-            args["reward_model"] = get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.reward_model"))
+            args["reward_model"] = get_save_dir(
+                get("top.model_name"), get("top.finetuning_type"), get("train.reward_model")
+            )
+            args["reward_model_type"] = "lora" if get("top.finetuning_type") == "lora" else "full"
 
         if args["stage"] == "dpo":
             args["dpo_beta"] = get("train.dpo_beta")
@@ -154,9 +160,9 @@ class Runner:
         user_config = load_config()
 
         if get("top.checkpoints"):
-            checkpoint_dir = ",".join([
+            checkpoint_dir = ",".join([get_save_dir(
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints")
+                get("top.model_name"), get("top.finetuning_type"), ckpt
-            ])
+            ) for ckpt in get("top.checkpoints")])
             output_dir = get_save_dir(
                 get("top.model_name"), get("top.finetuning_type"), "eval_" + "_".join(get("top.checkpoints"))
             )
@@ -196,7 +202,7 @@ class Runner:
         return args
 
     def _preview(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
-        error = self._initialize(data, do_train)
+        error = self._initialize(data, do_train, from_preview=True)
         if error:
             gr.Warning(error)
             yield error, gr.update(visible=False)
@@ -205,16 +211,14 @@ class Runner:
             yield gen_cmd(args), gr.update(visible=False)
 
     def _launch(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
-        error = self._initialize(data, do_train)
+        error = self._initialize(data, do_train, from_preview=False)
         if error:
             gr.Warning(error)
             yield error, gr.update(visible=False)
         else:
             args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
             run_kwargs = dict(args=args, callbacks=[self.trainer_callback])
-            self.running = True
             self.do_train, self.running_data = do_train, data
-            self.monitor_inputs = dict(lang=data[self.manager.get_elem_by_name("top.lang")], output_dir=args["output_dir"])
             self.thread = Thread(target=run_exp, kwargs=run_kwargs)
             self.thread.start()
             yield from self.monitor()
@@ -232,7 +236,12 @@ class Runner:
         yield from self._launch(data, do_train=False)
 
     def monitor(self) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
-        lang, output_dir = self.monitor_inputs["lang"], self.monitor_inputs["output_dir"]
+        get = lambda name: self.running_data[self.manager.get_elem_by_name(name)]
+        self.running = True
+        lang = get("top.lang")
+        output_dir = get_save_dir(get("top.model_name"), get("top.finetuning_type"), get(
+            "{}.output_dir".format("train" if self.do_train else "eval")
+        ))
         while self.thread.is_alive():
             time.sleep(2)
             if self.aborted:

src/llmtuner/webui/utils.py

@@ -1,17 +1,20 @@
 import os
 import json
 import gradio as gr
-import matplotlib.figure
-import matplotlib.pyplot as plt
 from typing import TYPE_CHECKING, Any, Dict
 from datetime import datetime
 
+from llmtuner.extras.packages import is_matplotlib_available
 from llmtuner.extras.ploting import smooth
 from llmtuner.webui.common import get_save_dir
 
 if TYPE_CHECKING:
     from llmtuner.extras.callbacks import LogCallback
 
+if is_matplotlib_available():
+    import matplotlib.figure
+    import matplotlib.pyplot as plt
+
 
 def update_process_bar(callback: "LogCallback") -> Dict[str, Any]:
     if not callback.max_steps:
@@ -56,7 +59,7 @@ def get_eval_results(path: os.PathLike) -> str:
     return "```json\n{}\n```\n".format(result)
 
 
-def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotlib.figure.Figure:
+def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> "matplotlib.figure.Figure":
     if not base_model:
         return
     log_file = get_save_dir(base_model, finetuning_type, output_dir, "trainer_log.jsonl")

tests/cal_lr.py

@@ -7,12 +7,13 @@ import fire
 import math
 import torch
 from tqdm import tqdm
+from typing import Optional
 from torch.utils.data import DataLoader
 from transformers import DataCollatorForSeq2Seq
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.data import get_dataset, preprocess_dataset
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.tuner.core import get_train_args, load_model_and_tokenizer
+from llmtuner.model import get_train_args, load_model_and_tokenizer
 
 
 BASE_LR = 3e-4      # 1.5e-4 for 30B-70B models
@@ -24,12 +25,14 @@ def calculate_lr(
     dataset: str,
     cutoff_len: int,  # i.e. maximum input length during training
     batch_size: int,  # total batch size, namely (batch size * gradient accumulation * world size)
-    is_mistral: bool # mistral model uses a smaller learning rate
+    is_mistral: bool, # mistral model uses a smaller learning rate,
+    dataset_dir: Optional[str] = "data"
 ):
     model_args, data_args, training_args, finetuning_args, _ = get_train_args(dict(
         stage="sft",
         model_name_or_path=model_name_or_path,
         dataset=dataset,
+        dataset_dir=dataset_dir,
         template="default",
         cutoff_len=cutoff_len,
         output_dir="dummy_dir"