update web UI, support rm predict #210

Former-commit-id: ed0e186a134de816d6a9278f4e47baa6250a52d1

src/llmtuner/dsets/preprocess.py

@@ -143,8 +143,10 @@ def preprocess_dataset(
     if stage == "pt":
         preprocess_function = preprocess_pretrain_dataset
     elif stage == "sft":
-        preprocess_function = preprocess_unsupervised_dataset \
+        if not training_args.predict_with_generate:
-            if training_args.predict_with_generate else preprocess_supervised_dataset
+            preprocess_function = preprocess_supervised_dataset
+        else:
+            preprocess_function = preprocess_unsupervised_dataset
     elif stage == "rm":
         preprocess_function = preprocess_pairwise_dataset
     elif stage == "ppo":

src/llmtuner/tuner/core/parser.py

@@ -54,7 +54,7 @@ def get_train_args(
     assert not (training_args.do_train and training_args.predict_with_generate), \
         "`predict_with_generate` cannot be set as True while training."
 
-    assert (not training_args.do_predict) or training_args.predict_with_generate, \
+    assert general_args.stage != "sft" or (not training_args.do_predict) or training_args.predict_with_generate, \
         "Please enable `predict_with_generate` to save model predictions."
 
     assert model_args.quantization_bit is None or finetuning_args.finetuning_type == "lora", \

src/llmtuner/tuner/core/trainer.py

@@ -4,7 +4,8 @@ from typing import Dict, Optional
 
 from transformers import Seq2SeqTrainer
 from transformers.trainer import TRAINING_ARGS_NAME
-from transformers.modeling_utils import unwrap_model
+from transformers.modeling_utils import PreTrainedModel, unwrap_model
+from peft import PeftModel
 
 from llmtuner.extras.constants import FINETUNING_ARGS_NAME, VALUE_HEAD_FILE_NAME
 from llmtuner.extras.logging import get_logger
@@ -49,9 +50,9 @@ class PeftTrainer(Seq2SeqTrainer):
         else:
             backbone_model = model
 
-        if self.finetuning_args.finetuning_type == "lora":
+        if isinstance(backbone_model, PeftModel): # LoRA tuning
             backbone_model.save_pretrained(output_dir, state_dict=get_state_dict(backbone_model))
-        else: # freeze/full tuning
+        elif isinstance(backbone_model, PreTrainedModel): # freeze/full tuning
             backbone_model.config.use_cache = True
             backbone_model.save_pretrained(
                 output_dir,
@@ -61,6 +62,8 @@ class PeftTrainer(Seq2SeqTrainer):
             backbone_model.config.use_cache = False
             if self.tokenizer is not None:
                 self.tokenizer.save_pretrained(output_dir)
+        else:
+            logger.warning("No model to save.")
 
         with open(os.path.join(output_dir, TRAINING_ARGS_NAME), "w", encoding="utf-8") as f:
             f.write(self.args.to_json_string() + "\n")
@@ -77,8 +80,8 @@ class PeftTrainer(Seq2SeqTrainer):
         model = unwrap_model(self.model)
         backbone_model = getattr(model, "pretrained_model") if hasattr(model, "pretrained_model") else model
 
-        if self.finetuning_args.finetuning_type == "lora":
+        if isinstance(backbone_model, PeftModel):
-            backbone_model.load_adapter(self.state.best_model_checkpoint, getattr(backbone_model, "active_adapter"))
+            backbone_model.load_adapter(self.state.best_model_checkpoint, backbone_model.active_adapter)
             if hasattr(model, "v_head") and load_valuehead_params(model, self.state.best_model_checkpoint):
                 model.v_head.load_state_dict({
                     "summary.weight": getattr(model, "reward_head_weight"),

src/llmtuner/tuner/rm/trainer.py

@@ -1,10 +1,17 @@
+import os
+import json
 import torch
 from typing import Dict, List, Optional, Tuple, Union
+from transformers.trainer import PredictionOutput
 from transformers.modeling_utils import PreTrainedModel
 
+from llmtuner.extras.logging import get_logger
 from llmtuner.tuner.core.trainer import PeftTrainer
 
 
+logger = get_logger(__name__)
+
+
 class PairwisePeftTrainer(PeftTrainer):
     r"""
     Inherits PeftTrainer to compute pairwise loss.
@@ -36,3 +43,26 @@ class PairwisePeftTrainer(PeftTrainer):
         r_accept, r_reject = values[:, -1].split(batch_size, dim=0)
         loss = -torch.log(torch.sigmoid(r_accept - r_reject)).mean()
         return (loss, [loss, r_accept, r_reject]) if return_outputs else loss
+
+    def save_predictions(
+        self,
+        predict_results: PredictionOutput
+    ) -> None:
+        r"""
+        Saves model predictions to `output_dir`.
+
+        A custom behavior that not contained in Seq2SeqTrainer.
+        """
+        if not self.is_world_process_zero():
+            return
+
+        output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
+        logger.info(f"Saving prediction results to {output_prediction_file}")
+
+        acc_scores, rej_scores = predict_results.predictions
+
+        with open(output_prediction_file, "w", encoding="utf-8") as writer:
+            res: List[str] = []
+            for acc_score, rej_score in zip(acc_scores, rej_scores):
+                res.append(json.dumps({"accept": round(float(acc_score), 2), "reject": round(float(rej_score), 2)}))
+            writer.write("\n".join(res))

src/llmtuner/tuner/rm/workflow.py

@@ -56,3 +56,10 @@ def run_rm(
         metrics = trainer.evaluate(metric_key_prefix="eval")
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
+
+    # Predict
+    if training_args.do_predict:
+        predict_results = trainer.predict(dataset, metric_key_prefix="predict")
+        trainer.log_metrics("predict", predict_results.metrics)
+        trainer.save_metrics("predict", predict_results.metrics)
+        trainer.save_predictions(predict_results)

src/llmtuner/webui/components/__init__.py

@@ -1,4 +1,5 @@
-from llmtuner.webui.components.eval import create_eval_tab
-from llmtuner.webui.components.infer import create_infer_tab
 from llmtuner.webui.components.top import create_top
 from llmtuner.webui.components.sft import create_sft_tab
+from llmtuner.webui.components.eval import create_eval_tab
+from llmtuner.webui.components.infer import create_infer_tab
+from llmtuner.webui.components.export import create_export_tab

src/llmtuner/webui/components/chatbot.py

@@ -22,13 +22,9 @@ def create_chat_box(
 
             with gr.Column(scale=1):
                 clear_btn = gr.Button()
-                max_new_tokens = gr.Slider(
+                max_new_tokens = gr.Slider(10, 2048, value=chat_model.generating_args.max_new_tokens, step=1)
-                    10, 2048, value=chat_model.generating_args.max_new_tokens, step=1, interactive=True
+                top_p = gr.Slider(0.01, 1, value=chat_model.generating_args.top_p, step=0.01)
-                )
+                temperature = gr.Slider(0.01, 1.5, value=chat_model.generating_args.temperature, step=0.01)
-                top_p = gr.Slider(0.01, 1, value=chat_model.generating_args.top_p, step=0.01, interactive=True)
-                temperature = gr.Slider(
-                    0.01, 1.5, value=chat_model.generating_args.temperature, step=0.01, interactive=True
-                )
 
     history = gr.State([])
 

src/llmtuner/webui/components/export.py

@@ -0,0 +1,34 @@
+from typing import Dict
+import gradio as gr
+from gradio.components import Component
+
+from llmtuner.webui.utils import export_model
+
+
+def create_export_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
+    with gr.Row():
+        save_dir = gr.Textbox()
+        max_shard_size = gr.Slider(value=10, minimum=1, maximum=100)
+
+    export_btn = gr.Button()
+    info_box = gr.Textbox(show_label=False, interactive=False)
+
+    export_btn.click(
+        export_model,
+        [
+            top_elems["lang"],
+            top_elems["model_name"],
+            top_elems["checkpoints"],
+            top_elems["finetuning_type"],
+            max_shard_size,
+            save_dir
+        ],
+        [info_box]
+    )
+
+    return dict(
+        save_dir=save_dir,
+        max_shard_size=max_shard_size,
+        export_btn=export_btn,
+        info_box=info_box
+    )

src/llmtuner/webui/components/sft.py

@@ -57,7 +57,7 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
 
     with gr.Row():
         with gr.Column(scale=4):
-            output_dir = gr.Textbox(interactive=True)
+            output_dir = gr.Textbox()
 
             with gr.Box():
                 output_box = gr.Markdown()

src/llmtuner/webui/interface.py

@@ -5,7 +5,8 @@ from llmtuner.webui.components import (
     create_top,
     create_sft_tab,
     create_eval_tab,
-    create_infer_tab
+    create_infer_tab,
+    create_export_tab
 )
 from llmtuner.webui.css import CSS
 from llmtuner.webui.manager import Manager
@@ -30,7 +31,10 @@ def create_ui() -> gr.Blocks:
         with gr.Tab("Chat"):
             infer_elems = create_infer_tab(top_elems)
 
-        elem_list = [top_elems, sft_elems, eval_elems, infer_elems]
+        with gr.Tab("Export"):
+            export_elems = create_export_tab(top_elems)
+
+        elem_list = [top_elems, sft_elems, eval_elems, infer_elems, export_elems]
         manager = Manager(elem_list)
 
         demo.load(

src/llmtuner/webui/locales.py

@@ -452,6 +452,34 @@ LOCALES = {
         "zh": {
             "label": "温度系数"
         }
+    },
+    "save_dir": {
+        "en": {
+            "label": "Export dir",
+            "info": "Directory to save exported model."
+        },
+        "zh": {
+            "label": "导出目录",
+            "info": "保存导出模型的文件夹路径。"
+        }
+    },
+    "max_shard_size": {
+        "en": {
+            "label": "Max shard size (GB)",
+            "info": "The maximum size for a model file."
+        },
+        "zh": {
+            "label": "最大分块大小（GB）",
+            "info": "模型文件的最大大小。"
+        }
+    },
+    "export_btn": {
+        "en": {
+            "value": "Export"
+        },
+        "zh": {
+            "value": "开始导出"
+        }
     }
 }
 
@@ -477,6 +505,14 @@ ALERTS = {
         "en": "Please choose a dataset.",
         "zh": "请选择数据集。"
     },
+    "err_no_checkpoint": {
+        "en": "Please select a checkpoint.",
+        "zh": "请选择断点。"
+    },
+    "err_no_save_dir": {
+        "en": "Please provide export dir.",
+        "zh": "请填写导出目录"
+    },
     "info_aborting": {
         "en": "Aborted, wait for terminating...",
         "zh": "训练中断，正在等待线程结束……"
@@ -504,5 +540,13 @@ ALERTS = {
     "info_unloaded": {
         "en": "Model unloaded.",
         "zh": "模型已卸载。"
+    },
+    "info_exporting": {
+        "en": "Exporting model...",
+        "zh": "正在导出模型……"
+    },
+    "info_exported": {
+        "en": "Model exported.",
+        "zh": "模型导出完成。"
     }
 }

src/llmtuner/webui/runner.py

@@ -3,7 +3,7 @@ import os
 import threading
 import time
 import transformers
-from typing import List, Optional, Tuple
+from typing import Generator, List, Optional, Tuple
 
 from llmtuner.extras.callbacks import LogCallback
 from llmtuner.extras.constants import DEFAULT_MODULE
@@ -25,7 +25,9 @@ class Runner:
         self.aborted = True
         self.running = False
 
-    def initialize(self, lang: str, model_name: str, dataset: list) -> Tuple[str, str, LoggerHandler, LogCallback]:
+    def initialize(
+        self, lang: str, model_name: str, dataset: list
+    ) -> Tuple[str, str, LoggerHandler, LogCallback]:
         if self.running:
             return None, ALERTS["err_conflict"][lang], None, None
 
@@ -50,7 +52,9 @@ class Runner:
 
         return model_name_or_path, "", logger_handler, trainer_callback
 
-    def finalize(self, lang: str, finish_info: Optional[str] = None) -> str:
+    def finalize(
+        self, lang: str, finish_info: Optional[str] = None
+    ) -> str:
         self.running = False
         torch_gc()
         if self.aborted:
@@ -87,7 +91,7 @@ class Runner:
         lora_dropout: float,
         lora_target: str,
         output_dir: str
-    ):
+    ) -> Generator[str, None, None]:
         model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
         if error:
             yield error
@@ -174,7 +178,7 @@ class Runner:
         max_samples: str,
         batch_size: int,
         predict: bool
-    ):
+    ) -> Generator[str, None, None]:
         model_name_or_path, error, logger_handler, trainer_callback = self.initialize(lang, model_name, dataset)
         if error:
             yield error

src/llmtuner/webui/utils.py

@@ -3,11 +3,13 @@ import json
 import gradio as gr
 import matplotlib.figure
 import matplotlib.pyplot as plt
-from typing import Any, Dict, Tuple
+from typing import Any, Dict, Generator, List, Tuple
 from datetime import datetime
 
 from llmtuner.extras.ploting import smooth
-from llmtuner.webui.common import get_save_dir, DATA_CONFIG
+from llmtuner.tuner import get_infer_args, load_model_and_tokenizer
+from llmtuner.webui.common import get_model_path, get_save_dir, DATA_CONFIG
+from llmtuner.webui.locales import ALERTS
 
 
 def format_info(log: str, tracker: dict) -> str:
@@ -83,3 +85,41 @@ def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotl
     ax.set_xlabel("step")
     ax.set_ylabel("loss")
     return fig
+
+
+def export_model(
+    lang: str, model_name: str, checkpoints: List[str], finetuning_type: str, max_shard_size: int, save_dir: str
+) -> Generator[str, None, None]:
+    if not model_name:
+        yield ALERTS["err_no_model"][lang]
+        return
+
+    model_name_or_path = get_model_path(model_name)
+    if not model_name_or_path:
+        yield ALERTS["err_no_path"][lang]
+        return
+
+    if not checkpoints:
+        yield ALERTS["err_no_checkpoint"][lang]
+        return
+
+    checkpoint_dir = ",".join(
+            [os.path.join(get_save_dir(model_name), finetuning_type, checkpoint) for checkpoint in checkpoints]
+        )
+
+    if not save_dir:
+        yield ALERTS["err_no_save_dir"][lang]
+        return
+
+    args = dict(
+        model_name_or_path=model_name_or_path,
+        checkpoint_dir=checkpoint_dir,
+        finetuning_type=finetuning_type
+    )
+
+    yield ALERTS["info_exporting"][lang]
+    model_args, _, finetuning_args, _ = get_infer_args(args)
+    model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
+    model.save_pretrained(save_dir, max_shard_size=str(max_shard_size)+"GB")
+    tokenizer.save_pretrained(save_dir)
+    yield ALERTS["info_exported"][lang]