release v0.1.0

Former-commit-id: 63c8d3a17cb18f0d8a8e37bfa147daf5bdd28ea9

src/llmtuner/webui/utils.py

@@ -0,0 +1,74 @@
+import os
+import json
+import gradio as gr
+import matplotlib.figure
+import matplotlib.pyplot as plt
+from typing import Tuple
+from datetime import datetime
+
+from llmtuner.extras.ploting import smooth
+from llmtuner.webui.common import get_save_dir, DATA_CONFIG
+
+
+def format_info(log: str, tracker: dict) -> str:
+    info = log
+    if "current_steps" in tracker:
+        info += "Running **{:d}/{:d}**: {} < {}\n".format(
+            tracker["current_steps"], tracker["total_steps"], tracker["elapsed_time"], tracker["remaining_time"]
+        )
+    return info
+
+
+def get_time() -> str:
+    return datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
+
+
+def can_preview(dataset_dir: str, dataset: list) -> dict:
+    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) -> Tuple[int, list, dict]:
+    with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
+        dataset_info = json.load(f)
+    data_file = dataset_info[dataset[0]]["file_name"]
+    with open(os.path.join(dataset_dir, data_file), "r", encoding="utf-8") as f:
+        data = json.load(f)
+    return len(data), data[:2], gr.update(visible=True)
+
+
+def get_eval_results(path: os.PathLike) -> str:
+    with open(path, "r", encoding="utf-8") as f:
+        result = json.dumps(json.load(f), indent=4)
+    return "```json\n{}\n```\n".format(result)
+
+
+def gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> matplotlib.figure.Figure:
+    log_file = os.path.join(get_save_dir(base_model), finetuning_type, output_dir, "trainer_log.jsonl")
+    if not os.path.isfile(log_file):
+        return None
+
+    plt.close("all")
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    steps, losses = [], []
+    with open(log_file, "r", encoding="utf-8") as f:
+        for line in f:
+            log_info = json.loads(line)
+            if log_info.get("loss", None):
+                steps.append(log_info["current_steps"])
+                losses.append(log_info["loss"])
+    ax.plot(steps, losses, alpha=0.4, label="original")
+    ax.plot(steps, smooth(losses), label="smoothed")
+    ax.legend()
+    ax.set_xlabel("step")
+    ax.set_ylabel("loss")
+    return fig