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[webui] upgrade webui and fix api (#8460)

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This commit is contained in:
Yaowei Zheng 2025-06-25 21:59:58 +08:00 committed by GitHub
parent f276b9a963
commit 4407231a3b
9 changed files with 109 additions and 27 deletions
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4
src/llamafactory/api/chat.py
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@ -132,7 +132,7 @@ def _process_request(
if re.match(r"^data:video\/(mp4|mkv|avi|mov);base64,(.+)$", video_url): # base64 video
video_stream = io.BytesIO(base64.b64decode(video_url.split(",", maxsplit=1)[1]))
elif os.path.isfile(video_url): # local file
video_stream = open(video_url, "rb")
video_stream = video_url
else: # web uri
video_stream = requests.get(video_url, stream=True).raw
@ -143,7 +143,7 @@ def _process_request(
if re.match(r"^data:audio\/(mpeg|mp3|wav|ogg);base64,(.+)$", audio_url): # base64 audio
audio_stream = io.BytesIO(base64.b64decode(audio_url.split(",", maxsplit=1)[1]))
elif os.path.isfile(audio_url): # local file
audio_stream = open(audio_url, "rb")
audio_stream = audio_url
else: # web uri
audio_stream = requests.get(audio_url, stream=True).raw

2
src/llamafactory/extras/logging.py
View File

@ -50,7 +50,7 @@ class LoggerHandler(logging.Handler):
def _write_log(self, log_entry: str) -> None:
with open(self.running_log, "a", encoding="utf-8") as f:
f.write(log_entry + "\n\n")
f.write(log_entry + "\n")
def emit(self, record) -> None:
if record.name == "httpx":

18
src/llamafactory/extras/misc.py
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@ -182,8 +182,22 @@ def get_logits_processor() -> "LogitsProcessorList":
return logits_processor
def get_current_memory() -> tuple[int, int]:
r"""Get the available and total memory for the current device (in Bytes)."""
if is_torch_xpu_available():
return torch.xpu.mem_get_info()
elif is_torch_npu_available():
return torch.npu.mem_get_info()
elif is_torch_mps_available():
return torch.mps.current_allocated_memory(), torch.mps.recommended_max_memory()
elif is_torch_cuda_available():
return torch.cuda.mem_get_info()
else:
return 0, -1
def get_peak_memory() -> tuple[int, int]:
r"""Get the peak memory usage for the current device (in Bytes)."""
r"""Get the peak memory usage (allocated, reserved) for the current device (in Bytes)."""
if is_torch_xpu_available():
return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
elif is_torch_npu_available():
@ -193,7 +207,7 @@ def get_peak_memory() -> tuple[int, int]:
elif is_torch_cuda_available():
return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
else:
return 0, 0
return 0, -1
def has_tokenized_data(path: "os.PathLike") -> bool:

2
src/llamafactory/webui/components/__init__.py
View File

@ -15,6 +15,7 @@
from .chatbot import create_chat_box
from .eval import create_eval_tab
from .export import create_export_tab
from .footer import create_footer
from .infer import create_infer_tab
from .top import create_top
from .train import create_train_tab
@ -24,6 +25,7 @@ __all__ = [
"create_chat_box",
"create_eval_tab",
"create_export_tab",
"create_footer",
"create_infer_tab",
"create_top",
"create_train_tab",

42
src/llamafactory/webui/components/footer.py Normal file
View File

@ -0,0 +1,42 @@
# 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.
from typing import TYPE_CHECKING
from ...extras.misc import get_current_memory
from ...extras.packages import is_gradio_available
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
def get_device_memory() -> "gr.Slider":
free, total = get_current_memory()
used = round((total - free) / (1024**3), 2)
total = round(total / (1024**3), 2)
return gr.Slider(minimum=0, maximum=total, value=used, step=0.01)
def create_footer() -> dict[str, "Component"]:
with gr.Row():
device_memory = gr.Slider(interactive=False)
timer = gr.Timer(value=5)
timer.tick(get_device_memory, outputs=[device_memory], queue=False)
return dict(device_memory=device_memory)

2
src/llamafactory/webui/control.py
View File

@ -112,7 +112,7 @@ def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> tup
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
running_log = "```\n" + f.read()[-20000:] + "\n```\n" # avoid lengthy log
trainer_log_path = os.path.join(output_path, TRAINER_LOG)
if os.path.isfile(trainer_log_path):

2
src/llamafactory/webui/interface.py
View File

@ -22,6 +22,7 @@ from .components import (
create_chat_box,
create_eval_tab,
create_export_tab,
create_footer,
create_infer_tab,
create_top,
create_train_tab,
@ -63,6 +64,7 @@ def create_ui(demo_mode: bool = False) -> "gr.Blocks":
with gr.Tab("Export"):
engine.manager.add_elems("export", create_export_tab(engine))
engine.manager.add_elems("footer", create_footer())
demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None)
lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False)
lang.input(save_config, inputs=[lang], queue=False)

22
src/llamafactory/webui/locales.py
View File

@ -2849,6 +2849,28 @@ LOCALES = {
"value": "エクスポート",
},
},
"device_memory": {
"en": {
"label": "Device memory",
"info": "Current memory usage of the device (GB).",
},
"ru": {
"label": "Память устройства",
"info": "Текущая память на устройстве (GB).",
},
"zh": {
"label": "设备显存",
"info": "当前设备的显存（GB）。",
},
"ko": {
"label": "디바이스 메모리",
"info": "지금 사용 중인 기기 메모리 (GB).",
},
"ja": {
"label": "デバイスメモリ",
"info": "現在のデバイスのメモリ（GB）。",
},
},
}

42
src/llamafactory/webui/runner.py
View File

@ -16,14 +16,13 @@ import json
import os
from collections.abc import Generator
from copy import deepcopy
from subprocess import Popen, TimeoutExpired
from subprocess import PIPE, Popen, TimeoutExpired
from typing import TYPE_CHECKING, Any, Optional
from transformers.trainer import TRAINING_ARGS_NAME
from transformers.utils import is_torch_npu_available
from ..extras.constants import LLAMABOARD_CONFIG, MULTIMODAL_SUPPORTED_MODELS, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_accelerator_available, torch_gc, use_ray
from ..extras.misc import is_accelerator_available, torch_gc
from ..extras.packages import is_gradio_available
from .common import (
DEFAULT_CACHE_DIR,
@ -114,7 +113,7 @@ class Runner:
return ""
def _finalize(self, lang: str, finish_info: str) -> str:
def _finalize(self, lang: str, finish_info: str) -> None:
r"""Clean the cached memory and resets the runner."""
finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
gr.Info(finish_info)
@ -123,7 +122,6 @@ class Runner:
self.running = False
self.running_data = None
torch_gc()
return finish_info
def _parse_train_args(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build and validate the training arguments."""
@ -314,11 +312,13 @@ class Runner:
max_samples=int(get("eval.max_samples")),
per_device_eval_batch_size=get("eval.batch_size"),
predict_with_generate=True,
report_to="none",
max_new_tokens=get("eval.max_new_tokens"),
top_p=get("eval.top_p"),
temperature=get("eval.temperature"),
output_dir=get_save_dir(model_name, finetuning_type, get("eval.output_dir")),
trust_remote_code=True,
ddp_timeout=180000000,
)
if get("eval.predict"):
@ -375,7 +375,7 @@ class Runner:
env["FORCE_TORCHRUN"] = "1"
# NOTE: DO NOT USE shell=True to avoid security risk
self.trainer = Popen(["llamafactory-cli", "train", save_cmd(args)], env=env)
self.trainer = Popen(["llamafactory-cli", "train", save_cmd(args)], env=env, stderr=PIPE, text=True)
yield from self.monitor()
def _build_config_dict(self, data: dict["Component", Any]) -> dict[str, Any]:
@ -417,7 +417,8 @@ class Runner:
swanlab_link = self.manager.get_elem_by_id("train.swanlab_link") if self.do_train else None
running_log = ""
while self.trainer is not None:
return_code = -1
while return_code == -1:
if self.aborted:
yield {
output_box: ALERTS["info_aborting"][lang],
@ -436,27 +437,26 @@ class Runner:
return_dict[swanlab_link] = running_info["swanlab_link"]
yield return_dict
try:
self.trainer.wait(2)
self.trainer = None
stderr = self.trainer.communicate(timeout=2)[1]
return_code = self.trainer.returncode
except TimeoutExpired:
continue
if self.do_train:
if os.path.exists(os.path.join(output_path, TRAINING_ARGS_NAME)) or use_ray():
finish_info = ALERTS["info_finished"][lang]
if return_code == 0:
finish_info = ALERTS["info_finished"][lang]
if self.do_train:
finish_log = ALERTS["info_finished"][lang] + "\n\n" + running_log
else:
finish_info = ALERTS["err_failed"][lang]
finish_log = load_eval_results(os.path.join(output_path, "all_results.json")) + "\n\n" + running_log
else:
if os.path.exists(os.path.join(output_path, "all_results.json")) or use_ray():
finish_info = load_eval_results(os.path.join(output_path, "all_results.json"))
else:
finish_info = ALERTS["err_failed"][lang]
print(stderr)
finish_info = ALERTS["err_failed"][lang]
finish_log = ALERTS["err_failed"][lang] + f" Exit code: {return_code}\n\n```\n{stderr}\n```\n"
return_dict = {
output_box: self._finalize(lang, finish_info) + "\n\n" + running_log,
progress_bar: gr.Slider(visible=False),
}
self._finalize(lang, finish_info)
return_dict = {output_box: finish_log, progress_bar: gr.Slider(visible=False)}
yield return_dict
def save_args(self, data):