Update configs.py

configs.py

@@ -14,174 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import dataclasses
-import os
-import sys
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, NewType, Optional, Tuple
 
-import transformers
-from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, HfArgumentParser
-
-
-MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-DataClassType = NewType("DataClassType", Any)
-
-
-class H4ArgumentParser(HfArgumentParser):
-    def parse_yaml_and_args(self, yaml_arg: str, other_args: Optional[List[str]] = None) -> List[dataclass]:
-        """
-        Parse a YAML file and overwrite the default/loaded values with the values provided to the command line.
-
-        Args:
-            yaml_arg (`str`):
-                The path to the config file used
-            other_args (`List[str]`, *optional`):
-                A list of strings to parse as command line arguments, e.g. ['--arg=val', '--arg2=val2'].
-
-        Returns:
-            [`List[dataclass]`]: a list of dataclasses with the values from the YAML file and the command line
-        """
-        arg_list = self.parse_yaml_file(os.path.abspath(yaml_arg))
-
-        outputs = []
-        # strip other args list into dict of key-value pairs
-        other_args = {arg.split("=")[0].strip("-"): arg.split("=")[1] for arg in other_args}
-        used_args = {}
-
-        # overwrite the default/loaded value with the value provided to the command line
-        # adapted from https://github.com/huggingface/transformers/blob/d0b5002378daabf62769159add3e7d66d3f83c3b/src/transformers/hf_argparser.py#L327
-        for data_yaml, data_class in zip(arg_list, self.dataclass_types):
-            keys = {f.name for f in dataclasses.fields(data_yaml) if f.init}
-            inputs = {k: v for k, v in vars(data_yaml).items() if k in keys}
-            for arg, val in other_args.items():
-                # add only if in keys
-                if arg in keys:
-                    base_type = data_yaml.__dataclass_fields__[arg].type
-                    inputs[arg] = val
-
-                    # cast type for ints, floats (default to strings)
-                    if base_type in [int, float]:
-                        inputs[arg] = base_type(val)
-
-                    if base_type == List[str]:
-                        inputs[arg] = [str(v) for v in val.split(",")]
-
-                    # bool of a non-empty string is True, so we manually check for bools
-                    if base_type == bool:
-                        if val in ["true", "True"]:
-                            inputs[arg] = True
-                        else:
-                            inputs[arg] = False
-
-                    # add to used-args so we can check if double add
-                    if arg not in used_args:
-                        used_args[arg] = val
-                    else:
-                        raise ValueError(f"Duplicate argument provided: {arg}, may cause unexpected behavior")
-
-            obj = data_class(**inputs)
-            outputs.append(obj)
-
-        return outputs
-
-    def parse(self) -> DataClassType | Tuple[DataClassType]:
-        if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
-            # If we pass only one argument to the script and it's the path to a YAML file,
-            # let's parse it to get our arguments.
-            output = self.parse_yaml_file(os.path.abspath(sys.argv[1]))
-        # parse command line args and yaml file
-        elif len(sys.argv) > 2 and sys.argv[1].endswith(".yaml"):
-            output = self.parse_yaml_and_args(os.path.abspath(sys.argv[1]), sys.argv[2:])
-        # parse command line args only
-        else:
-            output = self.parse_args_into_dataclasses()
-
-        if len(output) == 1:
-            output = output[0]
-        return output
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
-    """
-
-    base_model_revision: Optional[str] = field(
-        default=None,
-        metadata={"help": ("The base model checkpoint for weights initialization with PEFT adatpers.")},
-    )
-    model_name_or_path: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": (
-                "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
-            )
-        },
-    )
-    model_revision: str = field(
-        default="main",
-        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
-    )
-    model_code_revision: str = field(default=None, metadata={"help": "The branch of the IFT model"})
-    torch_dtype: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": (
-                "Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
-                "dtype will be automatically derived from the model's weights."
-            ),
-            "choices": ["auto", "bfloat16", "float16", "float32"],
-        },
-    )
-    trust_remote_code: bool = field(default=False, metadata={"help": "Trust remote code when loading a model."})
-    use_flash_attention_2: bool = field(
-        default=False,
-        metadata={
-            "help": (
-                "Whether to use flash attention 2. You must install this manually by running `pip install flash-attn --no-build-isolation`"
-            )
-        },
-    )
-    use_peft: bool = field(
-        default=False,
-        metadata={"help": ("Whether to use PEFT or not for training.")},
-    )
-    lora_r: Optional[int] = field(
-        default=16,
-        metadata={"help": ("LoRA R value.")},
-    )
-    lora_alpha: Optional[int] = field(
-        default=32,
-        metadata={"help": ("LoRA alpha.")},
-    )
-    lora_dropout: Optional[float] = field(
-        default=0.05,
-        metadata={"help": ("LoRA dropout.")},
-    )
-    lora_target_modules: Optional[List[str]] = field(
-        default=None,
-        metadata={"help": ("LoRA target modules.")},
-    )
-    lora_modules_to_save: Optional[List[str]] = field(
-        default=None,
-        metadata={"help": ("Model layers to unfreeze & train")},
-    )
-    load_in_8bit: bool = field(default=False, metadata={"help": "use 8 bit precision"})
-    load_in_4bit: bool = field(default=False, metadata={"help": "use 4 bit precision"})
-
-    bnb_4bit_quant_type: Optional[str] = field(
-        default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"}
-    )
-    use_bnb_nested_quant: bool = field(default=False, metadata={"help": "use nested quantization"})
-
-    def __post_init__(self):
-        if self.load_in_8bit and self.load_in_4bit:
-            raise ValueError("You can't use 8 bit and 4 bit precision at the same time")
-
 
 @dataclass
 class DataArguments:
@@ -222,51 +57,4 @@ class DataArguments:
     )
     truncation_side: Optional[str] = field(
         default=None, metadata={"help": "Truncation side to use for the tokenizer."}
-    )
-
-
-@dataclass
-class SFTConfig(transformers.TrainingArguments):
-    """
-    Arguments related to the training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
-    """
-
-    max_seq_length: Optional[int] = field(
-        default=None,
-        metadata={"help": ("Used by TRL for reward model training, which tries to read this parameter in init.")},
-    )
-    logging_first_step: bool = field(
-        default=True,
-        metadata={"help": ("Whether to log and evaluate the first global_step or not.")},
-    )
-    optim: Optional[str] = field(default="adamw_torch")
-
-
-@dataclass
-class DPOConfig(transformers.TrainingArguments):
-    """
-    Arguments related to the DPO training process itself. For all parameters, see: https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
-    """
-
-    beta: Optional[float] = field(
-        default=0.1,
-        metadata={"help": "The beta factor in DPO loss. Higher beta means less divergence from the initial policy."},
-    )
-    hub_model_revision: Optional[str] = field(
-        default="main",
-        metadata={"help": ("The Hub model branch to push the model to.")},
-    )
-    logging_first_step: bool = field(
-        default=True,
-        metadata={"help": ("Whether to log and evaluate the first global_step or not.")},
-    )
-    max_prompt_length: Optional[int] = field(
-        default=None,
-        metadata={"help": ("For DPO, the maximum length of the prompt to use for conditioning the model.")},
-    )
-    max_length: Optional[int] = field(
-        default=None,
-        metadata={"help": ("Used by TRL for reward model training, which tries to read this parameter in init.")},
-    )
-    optim: Optional[str] = field(default="rmsprop")
-    remove_unused_columns: bool = field(default=False)
+    )