simplify code

src/api_demo.py

@@ -5,9 +5,13 @@
 
 import uvicorn
 
-from llmtuner import create_app
+from llmtuner.api.app import create_app
+
+
+def main():
+    app = create_app()
+    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 
 if __name__ == "__main__":
-    app = create_app()
-    uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
+    main()

src/cli_demo.py

@@ -2,7 +2,8 @@
 # Implements stream chat in command line for fine-tuned models.
 # Usage: python cli_demo.py --model_name_or_path path_to_model --checkpoint_dir path_to_checkpoint
 
-from llmtuner import ChatModel, get_infer_args
+from llmtuner import ChatModel
+from llmtuner.tuner import get_infer_args
 
 
 def main():

src/export_model.py

@@ -2,7 +2,7 @@
 # Exports the fine-tuned model.
 # Usage: python export_model.py --checkpoint_dir path_to_checkpoint --output_dir path_to_save_model
 
-from llmtuner import get_train_args, load_model_and_tokenizer
+from llmtuner.tuner import get_train_args, load_model_and_tokenizer
 
 
 def main():

src/llmtuner/__init__.py

@@ -1,7 +1,4 @@
-from llmtuner.api import create_app
 from llmtuner.chat import ChatModel
-from llmtuner.tuner import get_train_args, get_infer_args, load_model_and_tokenizer, run_pt, run_sft, run_rm, run_ppo
-from llmtuner.webui import create_ui
 
 
 __version__ = "0.1.1"

src/llmtuner/api/__init__.py

@@ -1 +0,0 @@
-from llmtuner.api.app import create_app

src/llmtuner/chat/stream_chat.py

@@ -4,7 +4,7 @@ from threading import Thread
 from transformers import TextIteratorStreamer
 
 from llmtuner.extras.misc import get_logits_processor
-from llmtuner.extras.template import Template
+from llmtuner.extras.template import get_template
 from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments, GeneratingArguments
 from llmtuner.tuner import load_model_and_tokenizer
 
@@ -19,7 +19,7 @@ class ChatModel:
         generating_args: GeneratingArguments
     ) -> None:
         self.model, self.tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
-        self.template = Template(data_args.prompt_template)
+        self.template = get_template(data_args.prompt_template)
         self.source_prefix = data_args.source_prefix if data_args.source_prefix else ""
         self.generating_args = generating_args
 

src/llmtuner/dsets/__init__.py

@@ -1,2 +1,3 @@
 from llmtuner.dsets.loader import get_dataset
 from llmtuner.dsets.preprocess import preprocess_dataset
+from llmtuner.dsets.utils import split_dataset

src/llmtuner/dsets/callbacks.py

@@ -1,63 +0,0 @@
-import os
-import json
-import time
-from datetime import timedelta
-
-from transformers import (
-    TrainerCallback,
-    TrainerControl,
-    TrainerState,
-    TrainingArguments
-)
-
-
-class LogCallback(TrainerCallback):
-
-    def __init__(self, runner=None):
-        self.runner = runner
-        self.start_time = time.time()
-        self.tracker = {}
-
-    def on_step_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
-        r"""
-        Event called at the beginning of a training step. If using gradient accumulation, one training step
-        might take several inputs.
-        """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    def on_substep_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
-        r"""
-        Event called at the end of an substep during gradient accumulation.
-        """
-        if self.runner is not None and self.runner.aborted:
-            control.should_epoch_stop = True
-            control.should_training_stop = True
-
-    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs) -> None:
-        r"""
-        Event called after logging the last logs.
-        """
-        if "loss" not in state.log_history[-1]:
-            return
-        cur_time = time.time()
-        cur_steps = state.log_history[-1].get("step")
-        elapsed_time = cur_time - self.start_time
-        avg_time_per_step = elapsed_time / cur_steps if cur_steps != 0 else 0
-        remaining_steps = state.max_steps - cur_steps
-        remaining_time = remaining_steps * avg_time_per_step
-        self.tracker = {
-            "current_steps": cur_steps,
-            "total_steps": state.max_steps,
-            "loss": state.log_history[-1].get("loss", None),
-            "reward": state.log_history[-1].get("reward", None),
-            "learning_rate": state.log_history[-1].get("learning_rate", None),
-            "epoch": state.log_history[-1].get("epoch", None),
-            "percentage": round(cur_steps / state.max_steps * 100, 2) if state.max_steps != 0 else 100,
-            "elapsed_time": str(timedelta(seconds=int(elapsed_time))),
-            "remaining_time": str(timedelta(seconds=int(remaining_time)))
-        }
-        os.makedirs(args.output_dir, exist_ok=True)
-        with open(os.path.join(args.output_dir, "trainer_log.jsonl"), "a", encoding="utf-8") as f:
-            f.write(json.dumps(self.tracker) + "\n")

src/llmtuner/dsets/preprocess.py

@@ -6,7 +6,7 @@ from transformers.tokenization_utils import PreTrainedTokenizer
 from datasets import Dataset
 
 from llmtuner.extras.constants import IGNORE_INDEX
-from llmtuner.extras.template import Template
+from llmtuner.extras.template import get_template
 from llmtuner.hparams import DataArguments
 
 
@@ -19,7 +19,7 @@ def preprocess_dataset(
 ) -> Dataset:
 
     column_names = list(dataset.column_names)
-    prompt_template = Template(data_args.prompt_template)
+    prompt_template = get_template(data_args.prompt_template)
 
     # support question with a single answer or multiple answers
     def get_dialog(examples):

src/llmtuner/dsets/utils.py

@@ -0,0 +1,16 @@
+from typing import Dict
+from datasets import Dataset
+
+
+def split_dataset(
+    dataset: Dataset, dev_ratio: float, do_train: bool
+) -> Dict[str, Dataset]:
+    # Split the dataset
+    if do_train:
+        if dev_ratio > 1e-6:
+            dataset = dataset.train_test_split(test_size=dev_ratio)
+            return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
+        else:
+            return {"train_dataset": dataset}
+    else: # do_eval or do_predict
+        return {"eval_dataset": dataset}

src/llmtuner/extras/template.py

@@ -3,30 +3,13 @@ from dataclasses import dataclass
 
 
 @dataclass
-class Format:
+class Template:
+
     prefix: str
     prompt: str
     sep: str
     use_history: bool
 
-
-templates: Dict[str, Format] = {}
-
-
-@dataclass
-class Template:
-
-    name: str
-
-    def __post_init__(self):
-        if self.name in templates:
-            self.prefix = templates[self.name].prefix
-            self.prompt = templates[self.name].prompt
-            self.sep = templates[self.name].sep
-            self.use_history = templates[self.name].use_history
-        else:
-            raise ValueError("Template {} does not exist.".format(self.name))
-
     def get_prompt(
         self, query: str, history: Optional[List[Tuple[str, str]]] = None, prefix: Optional[str] = ""
     ) -> str:
@@ -61,8 +44,11 @@ class Template:
         return convs[:-1] # drop last
 
 
+templates: Dict[str, Template] = {}
+
+
 def register_template(name: str, prefix: str, prompt: str, sep: str, use_history: bool) -> None:
-    templates[name] = Format(
+    templates[name] = Template(
         prefix=prefix,
         prompt=prompt,
         sep=sep,
@@ -70,6 +56,12 @@ def register_template(name: str, prefix: str, prompt: str, sep: str, use_history
     )
 
 
+def get_template(name: str) -> Template:
+    template = templates.get(name, None)
+    assert template is not None, "Template {} does not exist.".format(name)
+    return template
+
+
 r"""
 Supports language model inference without histories.
 """

src/llmtuner/tuner/pt/workflow.py

@@ -4,7 +4,7 @@ import math
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.ploting import plot_loss
@@ -28,16 +28,6 @@ def run_pt(
         label_pad_token_id=IGNORE_INDEX if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
     )
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = PeftTrainer(
         finetuning_args=finetuning_args,
@@ -46,7 +36,7 @@ def run_pt(
         tokenizer=tokenizer,
         data_collator=data_collator,
         callbacks=callbacks,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Training

src/llmtuner/tuner/rm/workflow.py

@@ -5,7 +5,7 @@
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.ploting import plot_loss
 from llmtuner.hparams import ModelArguments, DataArguments, FinetuningArguments
@@ -29,16 +29,6 @@ def run_rm(
 
     training_args.remove_unused_columns = False # important for pairwise dataset
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = PairwisePeftTrainer(
         finetuning_args=finetuning_args,
@@ -48,7 +38,7 @@ def run_rm(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=compute_accuracy,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Training

src/llmtuner/tuner/sft/workflow.py

@@ -3,7 +3,7 @@
 from typing import Optional, List
 from transformers import Seq2SeqTrainingArguments, DataCollatorForSeq2Seq, TrainerCallback
 
-from llmtuner.dsets import get_dataset, preprocess_dataset
+from llmtuner.dsets import get_dataset, preprocess_dataset, split_dataset
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import IGNORE_INDEX
 from llmtuner.extras.misc import get_logits_processor
@@ -35,16 +35,6 @@ def run_sft(
     training_args.generation_num_beams = data_args.eval_num_beams if \
                 data_args.eval_num_beams is not None else training_args.generation_num_beams
 
-    # Split the dataset
-    if training_args.do_train:
-        if data_args.dev_ratio > 1e-6:
-            dataset = dataset.train_test_split(test_size=data_args.dev_ratio)
-            trainer_kwargs = {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
-        else:
-            trainer_kwargs = {"train_dataset": dataset}
-    else: # do_eval or do_predict
-        trainer_kwargs = {"eval_dataset": dataset}
-
     # Initialize our Trainer
     trainer = Seq2SeqPeftTrainer(
         finetuning_args=finetuning_args,
@@ -54,7 +44,7 @@ def run_sft(
         data_collator=data_collator,
         callbacks=callbacks,
         compute_metrics=ComputeMetrics(tokenizer) if training_args.predict_with_generate else None,
-        **trainer_kwargs
+        **split_dataset(dataset, data_args.dev_ratio, training_args.do_train)
     )
 
     # Keyword arguments for `model.generate`

src/llmtuner/webui/__init__.py

@@ -1 +0,0 @@
-from llmtuner.webui.interface import create_ui

src/train_bash.py

@@ -1,4 +1,4 @@
-from llmtuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
+from llmtuner.tuner import get_train_args, run_pt, run_sft, run_rm, run_ppo
 
 
 def main():

src/train_web.py

@@ -1,4 +1,4 @@
-from llmtuner import create_ui
+from llmtuner.webui.interface import create_ui
 
 
 def main():

src/web_demo.py

@@ -5,7 +5,7 @@
 import gradio as gr
 from transformers.utils.versions import require_version
 
-from llmtuner import get_infer_args
+from llmtuner.tuner import get_infer_args
 from llmtuner.webui.chat import WebChatModel
 from llmtuner.webui.components.chatbot import create_chat_box
 from llmtuner.webui.manager import Manager