rename package

Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08

src/llamafactory/webui/utils.py

@@ -0,0 +1,106 @@
+import json
+import os
+from datetime import datetime
+from typing import Any, Dict, List, Optional, Tuple
+
+from yaml import safe_dump
+
+from ..extras.constants import RUNNING_LOG, TRAINER_CONFIG, TRAINER_LOG
+from ..extras.packages import is_gradio_available, is_matplotlib_available
+from ..extras.ploting import gen_loss_plot
+from .locales import ALERTS
+
+
+if is_gradio_available():
+    import gradio as gr
+
+
+def can_quantize(finetuning_type: str) -> "gr.Dropdown":
+    if finetuning_type != "lora":
+        return gr.Dropdown(value="none", interactive=False)
+    else:
+        return gr.Dropdown(interactive=True)
+
+
+def check_json_schema(text: str, lang: str) -> None:
+    try:
+        tools = json.loads(text)
+        if tools:
+            assert isinstance(tools, list)
+            for tool in tools:
+                if "name" not in tool:
+                    raise NotImplementedError("Name not found.")
+    except NotImplementedError:
+        gr.Warning(ALERTS["err_tool_name"][lang])
+    except Exception:
+        gr.Warning(ALERTS["err_json_schema"][lang])
+
+
+def clean_cmd(args: Dict[str, Any]) -> Dict[str, Any]:
+    no_skip_keys = ["packing"]
+    return {k: v for k, v in args.items() if (k in no_skip_keys) or (v is not None and v is not False and v != "")}
+
+
+def gen_cmd(args: Dict[str, Any]) -> str:
+    current_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
+    cmd_lines = ["CUDA_VISIBLE_DEVICES={} llamafactory-cli train ".format(current_devices)]
+    for k, v in clean_cmd(args).items():
+        cmd_lines.append("    --{} {} ".format(k, str(v)))
+
+    cmd_text = "\\\n".join(cmd_lines)
+    cmd_text = "```bash\n{}\n```".format(cmd_text)
+    return cmd_text
+
+
+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 get_time() -> str:
+    return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
+
+
+def get_trainer_info(output_path: os.PathLike, do_train: bool) -> Tuple[str, "gr.Slider", Optional["gr.Plot"]]:
+    running_log = ""
+    running_progress = gr.Slider(visible=False)
+    running_loss = None
+
+    running_log_path = os.path.join(output_path, RUNNING_LOG)
+    if os.path.isfile(running_log_path):
+        with open(running_log_path, "r", encoding="utf-8") as f:
+            running_log = f.read()
+
+    trainer_log_path = os.path.join(output_path, TRAINER_LOG)
+    if os.path.isfile(trainer_log_path):
+        trainer_log: List[Dict[str, Any]] = []
+        with open(trainer_log_path, "r", encoding="utf-8") as f:
+            for line in f:
+                trainer_log.append(json.loads(line))
+
+        if len(trainer_log) != 0:
+            latest_log = trainer_log[-1]
+            percentage = latest_log["percentage"]
+            label = "Running {:d}/{:d}: {} < {}".format(
+                latest_log["current_steps"],
+                latest_log["total_steps"],
+                latest_log["elapsed_time"],
+                latest_log["remaining_time"],
+            )
+            running_progress = gr.Slider(label=label, value=percentage, visible=True)
+
+            if do_train and is_matplotlib_available():
+                running_loss = gr.Plot(gen_loss_plot(trainer_log))
+
+    return running_log, running_progress, running_loss
+
+
+def save_cmd(args: Dict[str, Any]) -> str:
+    output_dir = args["output_dir"]
+    os.makedirs(output_dir, exist_ok=True)
+
+    with open(os.path.join(output_dir, TRAINER_CONFIG), "w", encoding="utf-8") as f:
+        safe_dump(clean_cmd(args), f)
+
+    return os.path.join(output_dir, TRAINER_CONFIG)