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# Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from typing import Any, Dict, List, Optional, Tuple
from transformers.trainer_utils import get_last_checkpoint
from ..extras.constants import (
CHECKPOINT_NAMES,
PEFT_METHODS,
RUNNING_LOG,
STAGES_USE_PAIR_DATA,
SWANLAB_CONFIG,
TRAINER_LOG,
TRAINING_STAGES,
)
from ..extras.packages import is_gradio_available, is_matplotlib_available
from ..extras.ploting import gen_loss_plot
from ..model import QuantizationMethod
from .common import DEFAULT_CONFIG_DIR, DEFAULT_DATA_DIR, get_model_path, get_save_dir, get_template, load_dataset_info
from .locales import ALERTS
if is_gradio_available():
import gradio as gr
def can_quantize(finetuning_type: str) -> "gr.Dropdown":
r"""
Judges if the quantization is available in this finetuning type.
Inputs: top.finetuning_type
Outputs: top.quantization_bit
"""
if finetuning_type not in PEFT_METHODS:
return gr.Dropdown(value="none", interactive=False)
else:
return gr.Dropdown(interactive=True)
def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
r"""
Gets the available quantization bits.
Inputs: top.quantization_method
Outputs: top.quantization_bit
"""
if quantization_method == QuantizationMethod.BITS_AND_BYTES.value:
available_bits = ["none", "8", "4"]
elif quantization_method == QuantizationMethod.HQQ.value:
available_bits = ["none", "8", "6", "5", "4", "3", "2", "1"]
elif quantization_method == QuantizationMethod.EETQ.value:
available_bits = ["none", "8"]
return gr.Dropdown(choices=available_bits)
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> Tuple[List[str], bool]:
r"""
Modifys states after changing the training stage.
Inputs: train.training_stage
Outputs: train.dataset, train.packing
"""
return [], TRAINING_STAGES[training_stage] == "pt"
def get_model_info(model_name: str) -> Tuple[str, str]:
r"""
Gets the necessary information of this model.
Inputs: top.model_name
Outputs: top.model_path, top.template
"""
return get_model_path(model_name), get_template(model_name)
def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> Tuple[str, "gr.Slider", Dict[str, Any]]:
r"""
Gets training infomation for monitor.
If do_train is True:
Inputs: top.lang, train.output_path
Outputs: train.output_box, train.progress_bar, train.loss_viewer, train.swanlab_link
If do_train is False:
Inputs: top.lang, eval.output_path
Outputs: eval.output_box, eval.progress_bar, None, None
"""
running_log = ""
running_progress = gr.Slider(visible=False)
running_info = {}
running_log_path = os.path.join(output_path, RUNNING_LOG)
if os.path.isfile(running_log_path):
with open(running_log_path, encoding="utf-8") as f:
running_log = f.read()[-20000:] # avoid lengthy log
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, 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_info["loss_viewer"] = gr.Plot(gen_loss_plot(trainer_log))
swanlab_config_path = os.path.join(output_path, SWANLAB_CONFIG)
if os.path.isfile(swanlab_config_path):
with open(swanlab_config_path, encoding="utf-8") as f:
swanlab_public_config = json.load(f)
swanlab_link = swanlab_public_config["cloud"]["experiment_url"]
if swanlab_link is not None:
running_info["swanlab_link"] = gr.Markdown(
ALERTS["info_swanlab_link"][lang] + swanlab_link, visible=True
)
return running_log, running_progress, running_info
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
r"""
Lists all available checkpoints.
Inputs: top.model_name, top.finetuning_type
Outputs: top.checkpoint_path
"""
checkpoints = []
if model_name:
save_dir = get_save_dir(model_name, finetuning_type)
if save_dir and os.path.isdir(save_dir):
for checkpoint in os.listdir(save_dir):
if os.path.isdir(os.path.join(save_dir, checkpoint)) and any(
os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CHECKPOINT_NAMES
):
checkpoints.append(checkpoint)
if finetuning_type in PEFT_METHODS:
return gr.Dropdown(value=[], choices=checkpoints, multiselect=True)
else:
return gr.Dropdown(value=None, choices=checkpoints, multiselect=False)
def list_config_paths(current_time: str) -> "gr.Dropdown":
r"""
Lists all the saved configuration files.
Inputs: train.current_time
Outputs: train.config_path
"""
config_files = [f"{current_time}.yaml"]
if os.path.isdir(DEFAULT_CONFIG_DIR):
for file_name in os.listdir(DEFAULT_CONFIG_DIR):
if file_name.endswith(".yaml") and file_name not in config_files:
config_files.append(file_name)
return gr.Dropdown(choices=config_files)
def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
r"""
Lists all available datasets in the dataset dir for the training stage.
Inputs: *.dataset_dir, *.training_stage
Outputs: *.dataset
"""
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
ranking = TRAINING_STAGES[training_stage] in STAGES_USE_PAIR_DATA
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
return gr.Dropdown(choices=datasets)
def list_output_dirs(model_name: Optional[str], finetuning_type: str, current_time: str) -> "gr.Dropdown":
r"""
Lists all the directories that can resume from.
Inputs: top.model_name, top.finetuning_type, train.current_time
Outputs: train.output_dir
"""
output_dirs = [f"train_{current_time}"]
if model_name:
save_dir = get_save_dir(model_name, finetuning_type)
if save_dir and os.path.isdir(save_dir):
for folder in os.listdir(save_dir):
output_dir = os.path.join(save_dir, folder)
if os.path.isdir(output_dir) and get_last_checkpoint(output_dir) is not None:
output_dirs.append(folder)
return gr.Dropdown(choices=output_dirs)
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