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import logging
import os
import time
from threading import Thread
from typing import TYPE_CHECKING, Any, Dict, Generator, Optional, Tuple
import gradio as gr
import transformers
from gradio.components import Component # cannot use TYPE_CHECKING here
from transformers.trainer import TRAINING_ARGS_NAME
from ..extras.callbacks import LogCallback
from ..extras.constants import TRAINING_STAGES
from ..extras.logging import LoggerHandler
from ..extras.misc import get_device_count, torch_gc
from ..train import run_exp
from .common import get_module, get_save_dir, load_config
from .locales import ALERTS
from .utils import gen_cmd, get_eval_results, update_process_bar
if TYPE_CHECKING:
from .manager import Manager
class Runner:
def __init__(self, manager: "Manager", demo_mode: Optional[bool] = False) -> None:
self.manager = manager
self.demo_mode = demo_mode
""" Resume """
self.thread: "Thread" = None
self.do_train = True
self.running_data: Dict["Component", Any] = None
""" State """
self.aborted = False
self.running = False
""" Handler """
self.logger_handler = LoggerHandler()
self.logger_handler.setLevel(logging.INFO)
logging.root.addHandler(self.logger_handler)
transformers.logging.add_handler(self.logger_handler)
@property
def alive(self) -> bool:
return self.thread is not None
def set_abort(self) -> None:
self.aborted = True
def _initialize(self, data: Dict[Component, Any], do_train: bool, from_preview: bool) -> str:
get = lambda name: data[self.manager.get_elem_by_name(name)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
dataset = get("train.dataset") if do_train else get("eval.dataset")
if self.running:
return ALERTS["err_conflict"][lang]
if not model_name:
return ALERTS["err_no_model"][lang]
if not model_path:
return ALERTS["err_no_path"][lang]
if len(dataset) == 0:
return ALERTS["err_no_dataset"][lang]
if self.demo_mode and (not from_preview):
return ALERTS["err_demo"][lang]
if not from_preview and get_device_count() > 1:
return ALERTS["err_device_count"][lang]
self.aborted = False
self.logger_handler.reset()
self.trainer_callback = LogCallback(self)
return ""
def _finalize(self, lang: str, finish_info: str) -> str:
self.thread = None
self.running_data = None
self.running = False
torch_gc()
if self.aborted:
return ALERTS["info_aborted"][lang]
else:
return finish_info
def _parse_train_args(self, data: Dict[Component, Any]) -> Dict[str, Any]:
get = lambda name: data[self.manager.get_elem_by_name(name)]
user_config = load_config()
if get("top.adapter_path"):
adapter_name_or_path = ",".join(
[
get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
for adapter in get("top.adapter_path")
]
)
else:
adapter_name_or_path = None
args = dict(
stage=TRAINING_STAGES[get("train.training_stage")],
do_train=True,
model_name_or_path=get("top.model_path"),
adapter_name_or_path=adapter_name_or_path,
cache_dir=user_config.get("cache_dir", None),
finetuning_type=get("top.finetuning_type"),
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
flash_attn=(get("top.booster") == "flash_attn"),
use_unsloth=(get("top.booster") == "unsloth"),
dataset_dir=get("train.dataset_dir"),
dataset=",".join(get("train.dataset")),
cutoff_len=get("train.cutoff_len"),
learning_rate=float(get("train.learning_rate")),
num_train_epochs=float(get("train.num_train_epochs")),
max_samples=int(get("train.max_samples")),
per_device_train_batch_size=get("train.batch_size"),
gradient_accumulation_steps=get("train.gradient_accumulation_steps"),
lr_scheduler_type=get("train.lr_scheduler_type"),
max_grad_norm=float(get("train.max_grad_norm")),
logging_steps=get("train.logging_steps"),
save_steps=get("train.save_steps"),
warmup_steps=get("train.warmup_steps"),
neftune_noise_alpha=get("train.neftune_alpha") or None,
sft_packing=get("train.sft_packing"),
upcast_layernorm=get("train.upcast_layernorm"),
lora_rank=get("train.lora_rank"),
lora_dropout=get("train.lora_dropout"),
lora_target=get("train.lora_target") or get_module(get("top.model_name")),
additional_target=get("train.additional_target") or None,
create_new_adapter=get("train.create_new_adapter"),
output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.output_dir")),
fp16=(get("train.compute_type") == "fp16"),
bf16=(get("train.compute_type") == "bf16"),
)
args["disable_tqdm"] = True
if TRAINING_STAGES[get("train.training_stage")] in ["rm", "ppo", "dpo"]:
args["create_new_adapter"] = args["quantization_bit"] is None
if args["stage"] == "ppo":
args["reward_model"] = get_save_dir(
get("top.model_name"), get("top.finetuning_type"), get("train.reward_model")
)
args["reward_model_type"] = "lora" if get("top.finetuning_type") == "lora" else "full"
if args["stage"] == "dpo":
args["dpo_beta"] = get("train.dpo_beta")
args["dpo_ftx"] = get("train.dpo_ftx")
if get("train.val_size") > 1e-6 and args["stage"] != "ppo":
args["val_size"] = get("train.val_size")
args["evaluation_strategy"] = "steps"
args["eval_steps"] = get("train.save_steps")
args["load_best_model_at_end"] = True
return args
def _parse_eval_args(self, data: Dict[Component, Any]) -> Dict[str, Any]:
get = lambda name: data[self.manager.get_elem_by_name(name)]
user_config = load_config()
if get("top.adapter_path"):
adapter_name_or_path = ",".join(
[
get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
for adapter in get("top.adapter_path")
]
)
else:
adapter_name_or_path = None
args = dict(
stage="sft",
model_name_or_path=get("top.model_path"),
adapter_name_or_path=adapter_name_or_path,
cache_dir=user_config.get("cache_dir", None),
finetuning_type=get("top.finetuning_type"),
quantization_bit=int(get("top.quantization_bit")) if get("top.quantization_bit") in ["8", "4"] else None,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
flash_attn=(get("top.booster") == "flash_attn"),
use_unsloth=(get("top.booster") == "unsloth"),
dataset_dir=get("eval.dataset_dir"),
dataset=",".join(get("eval.dataset")),
cutoff_len=get("eval.cutoff_len"),
max_samples=int(get("eval.max_samples")),
per_device_eval_batch_size=get("eval.batch_size"),
predict_with_generate=True,
max_new_tokens=get("eval.max_new_tokens"),
top_p=get("eval.top_p"),
temperature=get("eval.temperature"),
output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("eval.output_dir")),
)
if get("eval.predict"):
args["do_predict"] = True
else:
args["do_eval"] = True
return args
def _preview(
self, data: Dict[Component, Any], do_train: bool
) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
error = self._initialize(data, do_train, from_preview=True)
if error:
gr.Warning(error)
yield error, gr.update(visible=False)
else:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
yield gen_cmd(args), gr.update(visible=False)
def _launch(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
error = self._initialize(data, do_train, from_preview=False)
if error:
gr.Warning(error)
yield error, gr.update(visible=False)
else:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
run_kwargs = dict(args=args, callbacks=[self.trainer_callback])
self.do_train, self.running_data = do_train, data
self.thread = Thread(target=run_exp, kwargs=run_kwargs)
self.thread.start()
yield from self.monitor()
def preview_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
yield from self._preview(data, do_train=True)
def preview_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
yield from self._preview(data, do_train=False)
def run_train(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
yield from self._launch(data, do_train=True)
def run_eval(self, data: Dict[Component, Any]) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
yield from self._launch(data, do_train=False)
def monitor(self) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
get = lambda name: self.running_data[self.manager.get_elem_by_name(name)]
self.running = True
lang = get("top.lang")
output_dir = get_save_dir(
get("top.model_name"),
get("top.finetuning_type"),
get("{}.output_dir".format("train" if self.do_train else "eval")),
)
while self.thread.is_alive():
time.sleep(2)
if self.aborted:
yield ALERTS["info_aborting"][lang], gr.update(visible=False)
else:
yield self.logger_handler.log, update_process_bar(self.trainer_callback)
if self.do_train:
if os.path.exists(os.path.join(output_dir, TRAINING_ARGS_NAME)):
finish_info = ALERTS["info_finished"][lang]
else:
finish_info = ALERTS["err_failed"][lang]
else:
if os.path.exists(os.path.join(output_dir, "all_results.json")):
finish_info = get_eval_results(os.path.join(output_dir, "all_results.json"))
else:
finish_info = ALERTS["err_failed"][lang]
yield self._finalize(lang, finish_info), gr.update(visible=False)
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