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support pagination in webui preview

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hiyouga
2023-11-02 21:21:45 +08:00
parent 34d8b2e56c
commit c1edb0cf1b
10 changed files with 201 additions and 154 deletions
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76
src/llmtuner/webui/utils.py
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@@ -3,13 +3,11 @@ import json
import gradio as gr
import matplotlib.figure
import matplotlib.pyplot as plt
from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
from typing import TYPE_CHECKING, Any, Dict
from datetime import datetime
from llmtuner.extras.ploting import smooth
from llmtuner.tuner import export_model
from llmtuner.webui.common import get_save_dir, DATA_CONFIG
from llmtuner.webui.locales import ALERTS
from llmtuner.webui.common import get_save_dir
if TYPE_CHECKING:
from llmtuner.extras.callbacks import LogCallback
@@ -33,37 +31,6 @@ def get_time() -> str:
return datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
def can_preview(dataset_dir: str, dataset: list) -> Dict[str, Any]:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
dataset_info = json.load(f)
if (
len(dataset) > 0
and "file_name" in dataset_info[dataset[0]]
and os.path.isfile(os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"]))
):
return gr.update(interactive=True)
else:
return gr.update(interactive=False)
def get_preview(
dataset_dir: str, dataset: list, start: Optional[int] = 0, end: Optional[int] = 2
) -> Tuple[int, list, Dict[str, Any]]:
with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
dataset_info = json.load(f)
data_file: str = dataset_info[dataset[0]]["file_name"]
with open(os.path.join(dataset_dir, data_file), "r", encoding="utf-8") as f:
if data_file.endswith(".json"):
data = json.load(f)
elif data_file.endswith(".jsonl"):
data = [json.loads(line) for line in f]
else:
data = [line for line in f]
return len(data), data[start:end], gr.update(visible=True)
def can_quantize(finetuning_type: str) -> Dict[str, Any]:
if finetuning_type != "lora":
return gr.update(value="None", interactive=False)
@@ -116,42 +83,3 @@ def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotl
ax.set_xlabel("step")
ax.set_ylabel("loss")
return fig
def save_model(
lang: str,
model_name: str,
model_path: str,
checkpoints: List[str],
finetuning_type: str,
template: str,
max_shard_size: int,
export_dir: str
) -> Generator[str, None, None]:
if not model_name:
yield ALERTS["err_no_model"][lang]
return
if not model_path:
yield ALERTS["err_no_path"][lang]
return
if not checkpoints:
yield ALERTS["err_no_checkpoint"][lang]
return
if not export_dir:
yield ALERTS["err_no_export_dir"][lang]
return
args = dict(
model_name_or_path=model_path,
checkpoint_dir=",".join([get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]),
finetuning_type=finetuning_type,
template=template,
export_dir=export_dir
)
yield ALERTS["info_exporting"][lang]
export_model(args, max_shard_size="{}GB".format(max_shard_size))
yield ALERTS["info_exported"][lang]