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support lora target auto find

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Former-commit-id: bce9984733d88bf013847eed523d1c75fdf0995e
This commit is contained in:
hiyouga
2023-09-09 15:38:37 +08:00
parent 50e93392dd
commit 7143c551ab
11 changed files with 117 additions and 72 deletions
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28
src/llmtuner/webui/runner.py
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@@ -12,7 +12,7 @@ from llmtuner.extras.constants import DEFAULT_MODULE, TRAINING_STAGES
from llmtuner.extras.logging import LoggerHandler
from llmtuner.extras.misc import torch_gc
from llmtuner.tuner import run_exp
from llmtuner.webui.common import get_model_path, get_save_dir
from llmtuner.webui.common import get_model_path, get_save_dir, load_config
from llmtuner.webui.locales import ALERTS
from llmtuner.webui.utils import gen_cmd, get_eval_results, update_process_bar
@@ -97,21 +97,25 @@ class Runner:
) -> Tuple[str, str, List[str], str, Dict[str, Any]]:
if checkpoints:
checkpoint_dir = ",".join(
[os.path.join(get_save_dir(model_name), finetuning_type, ckpt) for ckpt in checkpoints]
[get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]
)
else:
checkpoint_dir = None
output_dir = os.path.join(get_save_dir(model_name), finetuning_type, output_dir)
output_dir = get_save_dir(model_name, finetuning_type, output_dir)
user_config = load_config()
cache_dir = user_config.get("cache_dir", None)
args = dict(
stage=TRAINING_STAGES[training_stage],
model_name_or_path=get_model_path(model_name),
do_train=True,
overwrite_cache=True,
overwrite_cache=False,
cache_dir=cache_dir,
checkpoint_dir=checkpoint_dir,
finetuning_type=finetuning_type,
quantization_bit=int(quantization_bit) if quantization_bit and quantization_bit != "None" else None,
quantization_bit=int(quantization_bit) if quantization_bit in ["8", "4"] else None,
template=template,
system_prompt=system_prompt,
dataset_dir=dataset_dir,
@@ -172,22 +176,26 @@ class Runner:
) -> Tuple[str, str, List[str], str, Dict[str, Any]]:
if checkpoints:
checkpoint_dir = ",".join(
[os.path.join(get_save_dir(model_name), finetuning_type, checkpoint) for checkpoint in checkpoints]
[get_save_dir(model_name, finetuning_type, ckpt) for ckpt in checkpoints]
)
output_dir = os.path.join(get_save_dir(model_name), finetuning_type, "eval_" + "_".join(checkpoints))
output_dir = get_save_dir(model_name, finetuning_type, "eval_" + "_".join(checkpoints))
else:
checkpoint_dir = None
output_dir = os.path.join(get_save_dir(model_name), finetuning_type, "eval_base")
output_dir = get_save_dir(model_name, finetuning_type, "eval_base")
user_config = load_config()
cache_dir = user_config.get("cache_dir", None)
args = dict(
stage="sft",
model_name_or_path=get_model_path(model_name),
do_eval=True,
overwrite_cache=True,
overwrite_cache=False,
predict_with_generate=True,
cache_dir=cache_dir,
checkpoint_dir=checkpoint_dir,
finetuning_type=finetuning_type,
quantization_bit=int(quantization_bit) if quantization_bit and quantization_bit != "None" else None,
quantization_bit=int(quantization_bit) if quantization_bit in ["8", "4"] else None,
template=template,
system_prompt=system_prompt,
dataset_dir=dataset_dir,