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import json
import os
from datetime import datetime
from typing import TYPE_CHECKING, Any, Dict
import gradio as gr
from ..extras.packages import is_matplotlib_available
from ..extras.ploting import smooth
from .common import get_save_dir
from .locales import ALERTS
if TYPE_CHECKING:
from ..extras.callbacks import LogCallback
if is_matplotlib_available():
import matplotlib.figure
import matplotlib.pyplot as plt
def update_process_bar(callback: "LogCallback") -> Dict[str, Any]:
if not callback.max_steps:
return gr.update(visible=False)
percentage = round(100 * callback.cur_steps / callback.max_steps, 0) if callback.max_steps != 0 else 100.0
label = "Running {:d}/{:d}: {} < {}".format(
callback.cur_steps, callback.max_steps, callback.elapsed_time, callback.remaining_time
)
return gr.update(label=label, value=percentage, visible=True)
def get_time() -> str:
return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
def can_quantize(finetuning_type: str) -> Dict[str, Any]:
if finetuning_type != "lora":
return gr.update(value="None", interactive=False)
else:
return gr.update(interactive=True)
def check_json_schema(text: str, lang: str) -> None:
try:
tools = json.loads(text)
for tool in tools:
assert "name" in tool
except AssertionError:
gr.Warning(ALERTS["err_tool_name"][lang])
except json.JSONDecodeError:
gr.Warning(ALERTS["err_json_schema"][lang])
def gen_cmd(args: Dict[str, Any]) -> str:
args.pop("disable_tqdm", None)
args["plot_loss"] = args.get("do_train", None)
current_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
cmd_lines = ["CUDA_VISIBLE_DEVICES={} python src/train_bash.py ".format(current_devices)]
for k, v in args.items():
if v is not None and v is not False and v != "":
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 gen_plot(base_model: str, finetuning_type: str, output_dir: str) -> "matplotlib.figure.Figure":
if not base_model:
return
log_file = get_save_dir(base_model, finetuning_type, output_dir, "trainer_log.jsonl")
if not os.path.isfile(log_file):
return
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"])
if len(losses) == 0:
return None
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
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