disentangle model from tuner and rename modules

Former-commit-id: 02cbf91e7e424f8379c1fed01b82a5f7a83b6947

src/llmtuner/eval/__init__.py

@@ -1 +1 @@
-from llmtuner.eval.engine import Evaluator
+from llmtuner.eval.evaluator import Evaluator

src/llmtuner/eval/constants.py

@@ -1,3 +0,0 @@
-CHOICES = ["A", "B", "C", "D"]
-
-SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]

src/llmtuner/eval/evaluator.py

@@ -11,12 +11,10 @@ from typing import Any, Dict, List, Optional
 from datasets import load_dataset
 from transformers.utils import cached_file
 
-from llmtuner.eval.constants import CHOICES, SUBJECTS
-from llmtuner.eval.parser import get_eval_args
+from llmtuner.data.template import get_template_and_fix_tokenizer
 from llmtuner.eval.template import get_eval_template
-from llmtuner.extras.misc import dispatch_model
-from llmtuner.extras.template import get_template_and_fix_tokenizer
-from llmtuner.tuner.core import load_model_and_tokenizer
+from llmtuner.extras.constants import CHOICES, SUBJECTS
+from llmtuner.model import dispatch_model, get_eval_args, load_model_and_tokenizer
 
 
 class Evaluator:

src/llmtuner/eval/parser.py

@@ -1,49 +0,0 @@
-import transformers
-from typing import Any, Dict, Optional, Tuple
-from transformers import HfArgumentParser
-
-from llmtuner.extras.misc import parse_args
-from llmtuner.hparams import (
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-)
-
-
-def parse_eval_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-]:
-    parser = HfArgumentParser((
-        ModelArguments,
-        DataArguments,
-        EvaluationArguments,
-        FinetuningArguments
-    ))
-    return parse_args(parser, args)
-
-
-def get_eval_args(
-    args: Optional[Dict[str, Any]] = None
-) -> Tuple[
-    ModelArguments,
-    DataArguments,
-    EvaluationArguments,
-    FinetuningArguments
-]:
-    model_args, data_args, eval_args, finetuning_args = parse_eval_args(args)
-
-    if data_args.template is None:
-        raise ValueError("Please specify which `template` to use.")
-
-    if model_args.quantization_bit is not None and finetuning_args.finetuning_type != "lora":
-        raise ValueError("Quantization is only compatible with the LoRA method.")
-
-    transformers.set_seed(eval_args.seed)
-
-    return model_args, data_args, eval_args, finetuning_args

src/llmtuner/eval/template.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Dict, List, Tuple
 
-from llmtuner.eval.constants import CHOICES
+from llmtuner.extras.constants import CHOICES
 
 if TYPE_CHECKING:
     from datasets import Dataset