#!/usr/bin/env python
# coding=utf-8
"""This script defines dataclasses: ModelArguments and DatasetArguments,
that contain the arguments for the model and dataset used in training.
It imports several modules, including dataclasses, field from typing, Optional from typing,
require_version from transformers.utils.versions, MODEL_FOR_CAUSAL_LM_MAPPING,
and TrainingArguments from transformers.
MODEL_CONFIG_CLASSES is assigned a list of the model config classes from
MODEL_FOR_CAUSAL_LM_MAPPING. MODEL_TYPES is assigned a tuple of the model types
extracted from the MODEL_CONFIG_CLASSES.
"""
import logging
from dataclasses import dataclass, field, fields, Field, make_dataclass
from pathlib import Path
from typing import Optional, List, Union, Dict
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TrainingArguments,
)
from transformers.utils.versions import require_version
from lmflow.utils.versioning import is_flash_attn_available
[docs]
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
[docs]
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
[docs]
logger = logging.getLogger(__name__)
[docs]
class OptimizerNames():
[docs]
ADABELIEF = "adabelief"
[docs]
ADAMW_SCHEDULE_FREE = "adamw_schedule_free"
[docs]
SGD_SCHEDULE_FREE = "sgd_schedule_free"
@dataclass
[docs]
class ModelArguments:
"""
Define a class ModelArguments using the dataclass decorator.
The class contains several optional parameters that can be used to configure a model.
model_name_or_path : str
a string representing the path or name of a pretrained
model checkpoint for weights initialization. If None, a model will be trained from scratch.
model_type : str
a string representing the type of model to use if training from
scratch. If not provided, a pretrained model will be used.
config_overrides : str
a string representing the default config settings to override
when training a model from scratch.
config_name : str
a string representing the name or path of the pretrained config to
use, if different from the model_name_or_path.
tokenizer_name : str
a string representing the name or path of the pretrained tokenizer
to use, if different from the model_name_or_path.
cache_dir : str
a string representing the path to the directory where pretrained models
downloaded from huggingface.co will be stored.
use_fast_tokenizer : bool
a boolean indicating whether to use a fast tokenizer (backed by the
tokenizers library) or not.
model_revision : str
a string representing the specific model version to use (can be a
branch name, tag name, or commit id).
token : Optional[str]
Necessary when accessing a private model/dataset.
torch_dtype : str
a string representing the dtype to load the model under. If auto is
passed, the dtype will be automatically derived from the model's weights.
use_ram_optimized_load : bool
a boolean indicating whether to use disk mapping when memory is not
enough.
use_int8 : bool
a boolean indicating whether to load int8 quantization for inference.
load_in_4bit : bool
whether to load the model in 4bit
model_max_length : int
The maximum length of the model.
truncation_side : str
The side on which the model should have truncation applied.
arch_type : str
Model architecture type.
padding_side : str
The side on which the tokenizer should have padding applied.
eos_padding : bool
whether to pad with eos token instead of pad token.
ignore_bias_buffers : bool
fix for DDP issues with LM bias/mask buffers - invalid scalar type,`inplace operation.
"""
[docs]
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."
)
},
)
[docs]
lora_model_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The incremental model diff introduced by LoRA finetuning."
" Along with the original non-finetuned model forms the whole"
" finetuned model."
)
}
)
[docs]
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
[docs]
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
[docs]
arch_type: Optional[str] = field(
default="decoder_only",
metadata={
"help": ("Model architecture type."),
"choices": ["decoder_only", "encoder_decoder", "text_regression", "vision_encoder_decoder"],
},
)
[docs]
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
[docs]
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
[docs]
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
[docs]
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
[docs]
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
[docs]
token: Optional[str] = field(
default=None,
metadata={
"help": ("Necessary to specify when accessing a private model/dataset.")
},
)
[docs]
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust remote code when loading model."
)
},
)
[docs]
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"],
},
)
[docs]
use_dora: bool = field(
default=False,
metadata={"help": "Whether to dora, https://github.com/NVlabs/DoRA."},
)
[docs]
use_lora: bool = field(
default=False,
metadata={"help": "Whether to lora."},
)
[docs]
use_qlora: bool = field(
default=False,
metadata={"help": "Whether to use qlora."},
)
[docs]
bits: int = field(
default=4,
metadata={"help": "The number of bits for quantization.",
"choices": [4, 8], },
)
[docs]
quant_type: str = field(
default='nf4',
metadata={"help": "The quantization type for quantization.",
"choices": ["nf4", "fp4"], },
)
[docs]
double_quant: bool = field(
default=True,
metadata={"help": "Whether to use double quantization."},
)
[docs]
lora_r: int = field(
default=8,
metadata={"help": "the rank of the lora parameters. The smaller lora_r is , the fewer parameters lora has."},
)
[docs]
lora_alpha: int = field(
default=32,
metadata={
"help": "Merging ratio between the fine-tuned model and the original. This is controlled by a parameter called alpha in the paper."},
)
[docs]
lora_target_modules: List[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name",}
)
[docs]
lora_dropout: float = field(
default=0.1,
metadata={"help": "The dropout rate in lora.linear."},
)
[docs]
save_aggregated_lora: bool = field(
default=False,
metadata={"help": "Whether to save aggregated lora."},
)
[docs]
use_ram_optimized_load: bool = field(
default=True,
metadata={"help": "Whether use disk mapping when memory is not enough."}
)
[docs]
use_flash_attention: bool = field(
default=False,
metadata={
"help": (
"whether use flash attention layer to reduce GPU memory with"
" higher time cost."
)
}
)
[docs]
truncate_to_model_max_length: bool = field(
default=True,
metadata={
"help": (
"whether truncate the dataset to model max length."
)
}
)
[docs]
do_rope_scaling: bool = field(
default=False,
metadata={
"help": (
"whether do ROPE scaling for llama model."
"Linear_scaling credits to the Reddit user /u/kaiokendev."
"https://arxiv.org/abs/2306.15595"
"NTK_scaling credits to the Reddit users /u/bloc97 and /u/emozilla."
"https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/"
)
}
)
[docs]
rope_pi_ratio: int = field(
default=1,
metadata={
"help": (
"the ratio of pi in RoPE scaling."
)
}
)
[docs]
rope_ntk_ratio: int = field(
default=1,
metadata={
"help": (
"the ratio of NTK in RoPE scaling."
)
}
)
[docs]
use_int8: bool = field(
default=False,
metadata={"help": "whether to load int8 quantization for inference"}
)
[docs]
load_in_4bit: Optional[bool] = field(
default=True,
metadata={
"help": "whether to load the model in 4bit"
},
)
[docs]
model_max_length: Optional[int] = field(
default=None,
metadata={"help": (
"The maximum length of the model. When not specified, "
"will follow the model's default max length. (i.e., tokenizer.model_max_length)")
},
)
[docs]
truncation_side: str = field(
default=None,
metadata={
"help": (
"The side on which the tokenizer should have truncation applied. "
"When not specified, will follow the tokenizer's default truncation strategy. "
"(i.e., tokenizer.truncation_side)"),
"choices": [None, "left", "right"],
},
)
[docs]
padding_side: str = field(
default='right',
metadata={
"help": (
"The side on which the tokenizer should have padding applied. "
"LMFlow uses right padding by default. When set to `auto`, will "
"use padding_side from tokenizer.padding_side."),
"choices": ["right", "left", "auto"],
}
)
[docs]
eos_padding: Optional[bool] = field(
default=False,
metadata={"help": "whether to pad with eos token"}
)
[docs]
ignore_bias_buffers: Optional[bool] = field(
default=False,
metadata={
# debug argument for distributed training
"help": "fix for DDP issues with LM bias/mask buffers - invalid scalar type,`inplace operation. See"
"https://github.com/huggingface/transformers/issues/22482#issuecomment-1595790992"
},
)
[docs]
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
if self.use_qlora:
if not self.use_lora:
logger.warning("use_qlora is set to True, but use_lora is not set to True. Setting use_lora to True.")
self.use_lora = True
if self.use_flash_attention:
if not is_flash_attn_available():
self.use_flash_attention = False
logger.warning("Flash attention is not available in the current environment. Disabling flash attention.")
@dataclass
[docs]
class VisModelArguments(ModelArguments):
[docs]
low_resource: Optional[bool] = field(
default=False,
metadata={
"help": "Use 8 bit and float16 when loading llm"
}
)
[docs]
custom_model: bool = field(
default=False,
metadata={"help": "flag for the model from huggingface or not"}
)
[docs]
pretrained_language_projection_path: str = field(
default=None,
metadata={"help": "path for model pretrained_language_projection_path"}
)
[docs]
custom_vision_model: bool = field(
default=False,
metadata={"help": "flag for the model from huggingface or not"}
)
[docs]
image_encoder_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The name or path of the image encoder to use."
)
},
)
[docs]
llm_model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"llm model in multi-modality model"
)
},
)
[docs]
use_prompt_cache: bool = field(
default=False,
metadata={"help": "Whether to use prompt cache."},
)
[docs]
prompt_cache_path: Optional[str] = field(
default=None,
metadata={"help": "Path to prompt cache."},
)
[docs]
llava_loading: Optional[bool] = field(
default=False,
metadata={"help": "Whether to load module by module from pretrained model."},
)
[docs]
vision_select_layer: Optional[int] = field(
default=-2,
metadata={"help": "Which layer to select in vision model."},
)
[docs]
llava_pretrain_model_path: Optional[str] = field(
default=None,
metadata={"help": "Path to llava pretrained model."},
)
[docs]
save_pretrain_model_path: Optional[str] = field(
default=None,
metadata={"help": "Path to pretrained model."},
)
@dataclass
[docs]
class DatasetArguments:
"""
Define a class DatasetArguments using the dataclass decorator.
The class contains several optional parameters that can be used to configure a dataset for a language model.
dataset_path : str
a string representing the path of the dataset to use.
dataset_name : str
a string representing the name of the dataset to use. The default value is "customized".
is_custom_dataset : bool
a boolean indicating whether to use custom data. The default value is False.
customized_cache_dir : str
a string representing the path to the directory where customized dataset caches will be stored.
dataset_config_name : str
a string representing the configuration name of the dataset to use (via the datasets library).
train_file : str
a string representing the path to the input training data file (a text file).
validation_file : str
a string representing the path to the input evaluation data file to evaluate the perplexity on (a text file).
max_train_samples : int
an integer indicating the maximum number of training examples to use for debugging or quicker training.
If set, the training dataset will be truncated to this number.
max_eval_samples: int
an integer indicating the maximum number of evaluation examples to use for debugging or quicker training.
If set, the evaluation dataset will be truncated to this number.
streaming : bool
a boolean indicating whether to enable streaming mode.
block_size: int
an integer indicating the optional input sequence length after tokenization. The training dataset will be
truncated in blocks of this size for training.
train_on_prompt: bool
a boolean indicating whether to train on prompt for conversation datasets such as ShareGPT.
conversation_template: str
a string representing the template for conversation datasets.
The class also includes some additional parameters that can be used to configure the dataset further, such as `overwrite_cache`,
`validation_split_percentage`, `preprocessing_num_workers`, `disable_group_texts`, `demo_example_in_prompt`, `explanation_in_prompt`,
`keep_linebreaks`, and `prompt_structure`.
The field function is used to set default values and provide help messages for each parameter. The Optional type hint is
used to indicate that a parameter is optional. The metadata argument is used to provide additional information about
each parameter, such as a help message.
"""
[docs]
dataset_path: Optional[str] = field(
default=None, metadata={"help": "The path of the dataset to use."}
)
[docs]
dataset_name: Optional[str] = field(
default="customized", metadata={"help": "Should be \"customized\""}
)
[docs]
is_custom_dataset: Optional[bool] = field(
default=False, metadata={"help": "whether to use custom data"}
)
[docs]
customized_cache_dir: Optional[str] = field(
default=".cache/llm-ft/datasets",
metadata={"help": "Where do you want to store the customized dataset caches"},
)
[docs]
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
[docs]
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
[docs]
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
[docs]
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
[docs]
max_eval_samples: Optional[int] = field(
default=1e10,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
[docs]
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
[docs]
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
[docs]
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
[docs]
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
[docs]
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
[docs]
group_texts_batch_size: int = field(
default=1000,
metadata={
"help": (
"Number of samples that will be grouped together to go though"
" `group_texts` operation. See `--disable_group_texts` for"
" detailed explanation of this operation."
)
}
)
[docs]
disable_group_texts: bool = field(
default=True,
metadata={
"help": (
"Whether we disable group of original samples together to"
" generate sample sequences of length `block_size`"
" By Default, it is True, which means the long samples"
" are truncated to `block_size` tokens"
" and short samples are padded to `block_size` tokens."
" If set to False, we group every 1000 tokenized"
" sequences together, divide them into"
" [{total_num_tokens} / {block_size}] sequences,"
" each with `block_size` tokens"
" (the remaining tokens are ommited."
" This group text behavior is useful"
" for continual pretrain or pretrain."
)
},
)
[docs]
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
[docs]
test_file: Optional[str] = field(
default=None,
metadata={"help": "Evaluation File Path"},
)
[docs]
train_on_prompt: bool = field(
default=False,
metadata={"help": "Whether to train on prompt for conversation datasets such as ShareGPT."}
)
[docs]
conversation_template: Optional[str] = field(
default=None,
metadata={"help": "The template for conversation datasets."}
)
[docs]
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
@dataclass
[docs]
class MultiModalDatasetArguments(DatasetArguments):
[docs]
image_folder: Optional[str] = field(
default=None, metadata={"help": "The folder of the image file."}
)
[docs]
image_aspect_ratio: Optional[str] = field(
default="pad", metadata={"help": "The ratio type"}
)
[docs]
is_multimodal: Optional[bool] = field(
default=True, metadata={"help": "Flag for the modality type."}
)
[docs]
use_image_start_end: Optional[bool] = field(
default=True, metadata={"help": "Flag for the modality type."}
)
[docs]
sep_style: Optional[str] = field(
default="plain", metadata={"help": "Sep style in multi_modality dataset."}
)
@dataclass
[docs]
class FinetunerArguments(TrainingArguments):
"""
Adapt transformers.TrainingArguments
"""
[docs]
eval_dataset_path: Optional[str] = field(
default=None, metadata={"help": "The path of the eval dataset to use."}
)
[docs]
remove_unused_columns: Optional[bool] = field(
default=False,
metadata={
"help": "whether to remove the unused columns in collate fn"}
)
[docs]
finetune_part: Optional[str] = field(
default="language_projection",
metadata={
"help": "the module to finetune."
}
)
[docs]
save_language_projection: Optional[str] = field(
default=False,
metadata={
"help": "whether to save language projection layer in multi-modal models."
}
)
[docs]
use_lisa: bool = field(
default=False,
metadata={
"help": "whether to use LISA training strategy."
}
)
[docs]
lisa_activated_layers: int = field(
default=2,
metadata={
"help": "the number of activated layers in LISA."
}
)
[docs]
lisa_interval_steps: int = field(
default=20,
metadata={
"help": "the number of steps in each freezing interval of LISA, i.e. the selected unfreezed layers are randomly switched every {lisa_interval_steps} steps."
}
)
[docs]
lisa_layers_attribute: str = field(
default="model.model.layers",
metadata={
"help": "where the layer attribute stores, e.g. model.model.layers"
}
)
[docs]
use_customized_optim: bool = field(
default=False,
metadata={
"help": "whether to use customized optimizers."
}
)
[docs]
customized_optim: str = field(
default="sign_sgd",
metadata={
"help": "name of the customized optimizer."
}
)
[docs]
customized_optim_args: str = field(
default=None,
metadata={
"help": "optional arguments that are supplied."
}
)
[docs]
optim_dummy_beta1: float = field(
default=0.9,
metadata={
"help": "A useless argument for dummy optimizer, just for tutorial"
}
)
[docs]
optim_dummy_beta2: float = field(
default=0.999,
metadata={
"help": "A useless argument for dummy optimizer, just for tutorial"
}
)
[docs]
optim_adam_beta1: float = field(
default=0.9,
metadata={
"help": "Coefficient used for computing running averages of gradient"
}
)
[docs]
optim_adam_beta2: float = field(
default=0.999,
metadata={
"help": "Coefficient used for computing running averages of squared gradient"
}
)
[docs]
optim_beta1: float = field(
default=0.9,
metadata={
"help": "Coefficient used for computing running averages of gradient"
}
)
[docs]
optim_beta2: float = field(
default=0.999,
metadata={
"help": "Coefficient used for computing running averages of squared gradient"
}
)
[docs]
optim_beta3: float = field(
default=0.9,
metadata={
"help": "Coefficient used for computing running averages of gradient"
}
)
[docs]
optim_momentum: float = field(
default=0.999,
metadata={
"help": "Coefficient used for the momentum term in optimizers like SGD with momentum"
}
)
[docs]
optim_weight_decay: float = field(
default=0,
metadata={
"help": "Weight decay (L2 penalty) added to the loss to prevent overfitting"
}
)
@dataclass
[docs]
class RewardModelTunerArguments(FinetunerArguments):
"""
Arguments for reward modeling.
"""
pass
@dataclass
[docs]
class EvaluatorArguments:
"""
Define a class EvaluatorArguments using the dataclass decorator. The class contains several optional
parameters that can be used to configure a evaluator.
local_rank : str
For distributed training: local_rank
random_shuffle : bool
use_wandb : bool
random_seed : int, default = 1
output_dir : str, default = './output_dir',
mixed_precision : str, choice from ["bf16","fp16"].
mixed precision mode, whether to use bf16 or fp16
deepspeed :
Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already
loaded json file as a dict
temperature : float
An argument of model.generate in huggingface to control the diversity of generation.
repetition_penalty : float
An argument of model.generate in huggingface to penalize repetitions.
"""
[docs]
local_rank: int = field(
default=-1,
metadata={"help": "For distributed training: local_rank"
}
)
[docs]
random_shuffle: Optional[bool] = field(
default=False,
metadata={"help": ""
}
)
[docs]
use_wandb: Optional[bool] = field(
default=False,
metadata={
"help": (
"When this flag is True, wandb will be enabled"
)
},
)
[docs]
random_seed: Optional[int] = field(
default=1,
metadata={
"help": (
"used to set random seed"
)
},
)
[docs]
output_dir: Optional[str] = field(
default="./output_dir",
metadata={"help": "Output path for the inferenced results"},
)
[docs]
mixed_precision: Optional[str] = field(
default="bf16",
metadata={
"help": (
"mixed precision mode, whether to use bf16 or fp16"
),
"choices": ["bf16", "fp16"],
},
)
[docs]
deepspeed: Optional[str] = field(
default=None,
metadata={
"help": (
"Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already"
" loaded json file as a dict"
)
},
)
[docs]
answer_type: Optional[str] = field(
default="text",
metadata={
"help": (
'Question type for answer extraction from the decoder output.'
' Supported types: \n'
' 1) "multiple_choice", e.g. A, B, C, D, ...\n'
' 2) "binary_choice", e.g. yes, no, maybe\n'
' 3) "math", e.g. 1.0, -3.52\n'
' 4) "text", e.g. "I think that it is okay"\n'
' 5) Special treatment for several datasets\n'
' - "gsm8k"\n'
' - "svamp"\n'
' - "asdiv"\n'
' - "addsub"\n'
' - "singleeq"\n'
' - "multiarith"\n'
' - "aqua"\n'
' - "csqa"\n'
' - "strategyqa"\n'
' - "pubmedqa"\n'
' - "medmcqa"\n'
' - "usmle"\n'
)
},
)
[docs]
prompt_structure: Optional[str] = field(
default="{input}",
metadata={
"help": (
'Prompt structure to facilitate prompt engineering during'
' inference. The model will receive'
' `prompt_structure.format(input=input)` as its input.'
)
},
)
[docs]
evaluate_block_size: Optional[int] = field(
default=512,
metadata={
"help": (
"the model will have at least block_size tokens for context when calculating the conditional likelihood of any one token"
" (provided there are block_size preceding tokens available to condition on)"
)
},
)
[docs]
metric: Optional[str] = field(
default="accuracy",
metadata={
"help": "the metric the model will be evaluated on",
"choices": ["ppl", "perplexity", "acc", "accuracy", "nll", "neg_log_likelihood"],
},
)
[docs]
inference_batch_size_per_device: Optional[int] = field(
default=1,
metadata={
"help": (
"every device will infer {inference_batch_size_per_device}"
" samples in parallel. The inferred results will be concatenaed"
" with inputs and attach a reward."
),
},
)
[docs]
use_accelerator_for_evaluator: bool = field(
default=False, metadata={"help": "Whether to use Huggingface Accelerator instead of Deepspeed"},
)
[docs]
temperature: float = field(
default=0,
metadata={"help": "Temperature during inference."},
)
[docs]
repetition_penalty: float = field(
default=1,
metadata={"help": "Repetition_penalty during inference."},
)
[docs]
max_new_tokens: int = field(
default=100,
metadata={"help": "Maximum length during inference."},
)
@dataclass
[docs]
class InferencerArguments:
"""
Define a class InferencerArguments using the dataclass decorator. The class contains several optional
parameters that can be used to configure a inferencer.
local_rank : str
For distributed training: local_rank
random_seed : int, default = 1
inference_batch_size : int, default = 1
deepspeed :
Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already
loaded json file as a dict
mixed_precision : str, choice from ["bf16","fp16"].
mixed precision mode, whether to use bf16 or fp16
temperature : float
An argument of model.generate in huggingface to control the diversity of generation.
repetition_penalty : float
An argument of model.generate in huggingface to penalize repetitions.
use_beam_search : Optional[bool]
Whether to use beam search during inference, By default False.
num_output_sequences : Optional[int]
Number of output sequences to return for the given prompt,
currently only used in vllm inference, By default 8.
top_p : Optional[float]
top_p for sampling, By default 1.0.
top_k : Optional[int]
top_k for sampling, By default -1 (no top_k).
additional_stop_token_ids : Optional[List[int]]
the ids of the end of sentence tokens, By default [].
apply_chat_template : Optional[bool]
Whether to apply chat template, By default True.
save_results : Optional[bool]
Whether to save inference results, By default False.
results_path : Optional[str]
The **json file** path of inference results, By default None.
enable_decode_inference_result : Optional[bool]
Whether to detokenize the inference results.
NOTE: For iterative align pipelines, whether to detokenize depends on
the homogeneity of the policy model and the reward model
(i.e., if they have the same tokenizer).
use_vllm: bool, optional
Whether to use VLLM for inference, By default False.
vllm_tensor_parallel_size: int, optional
The tensor parallel size for VLLM inference.
vllm_gpu_memory_utilization: float, optional
The GPU memory utilization for VLLM inference. The proportion of GPU
memory (per GPU) to use for VLLM inference.
"""
[docs]
device: str = field(
default="gpu",
metadata={
"help": "device of chatbot",
"choices": ["gpu", "cpu"],
},
)
[docs]
local_rank: int = field(
default=-1,
metadata={"help": "For distributed training: local_rank"
},
)
[docs]
inference_batch_size: int = field(
default=1,
metadata={"help": "batch size for inference"},
)
[docs]
vllm_inference_batch_size: int = field(
default=1,
metadata={"help": "The batch size for VLLM inference."}
)
[docs]
temperature: float = field(
default=0.0,
metadata={"help": "Temperature during inference."},
)
[docs]
repetition_penalty: float = field(
default=1,
metadata={"help": "Repetition_penalty during inference."},
)
[docs]
max_new_tokens: int = field(
default=100,
metadata={"help": "Maximum length during inference."},
)
[docs]
random_seed: Optional[int] = field(
default=1,
metadata={
"help": (
"used to set random seed"
)
},
)
[docs]
deepspeed: Optional[str] = field(
default=None,
metadata={
"help": (
"Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already"
" loaded json file as a dict"
)
},
)
[docs]
mixed_precision: Optional[str] = field(
default="bf16",
metadata={
"help": (
"mixed precision mode, whether to use bf16 or fp16"
),
"choices": ["bf16", "fp16"],
},
)
[docs]
do_sample: Optional[bool] = field(
default=False,
metadata={
"help": "whether turn on true random sampling during inference."
},
)
[docs]
use_accelerator: bool = field(
default=False, metadata={"help": "Whether to use Huggingface Accelerator instead of Deepspeed"},
)
[docs]
use_beam_search: Optional[bool] = field(
default=False,
metadata={"help": "whether to use beam search during inference."},
)
[docs]
num_output_sequences: Optional[int] = field(
default=8,
metadata={"help": (
"number of output sequences to return for the given prompt, "
"currently only used in vllm inference."
)},
)
[docs]
top_p: Optional[float] = field(
default=1.0,
metadata={"help": "top_p for sampling."},
)
[docs]
top_k: Optional[int] = field(
default=-1,
metadata={"help": "top_k for sampling."},
)
[docs]
additional_stop_token_ids: Optional[List[int]] = field(
default_factory=lambda: [],
metadata={"help": "the ids of the end of sentence tokens"},
)
[docs]
apply_chat_template: Optional[bool] = field(
default=True,
metadata={"help": "whether to apply chat template"},
)
[docs]
enable_decode_inference_result: Optional[bool] = field(
default=False,
metadata={"help": "Whether to decode the inference results."},
)
[docs]
tensor_parallel_size: Optional[int] = field(
default=1,
metadata={"help": "The tp size for distributed (multi-instance) inference."}
)
[docs]
enable_distributed_inference: Optional[bool] = field(
default=False,
metadata={"help": "Whether to use multi-instance VLLM inference."}
)
[docs]
distributed_inference_num_instances: Optional[int] = field(
default=1,
metadata={"help": "The number of instances for multi-instance VLLM inference."}
)
# vllm inference args
[docs]
use_vllm: bool = field(
default=False,
metadata={"help": "Whether to use VLLM for inference, By default False."}
)
[docs]
vllm_tensor_parallel_size: Optional[int] = field(
default=1,
metadata={"help": "The tensor parallel size for VLLM inference."}
)
[docs]
vllm_gpu_memory_utilization: Optional[float] = field(
default=0.95,
metadata={"help": "The GPU memory utilization for VLLM inference."}
)
# Args for result saving
[docs]
save_results: Optional[bool] = field(
default=False, metadata={"help": "Whether to save inference results."}
)
[docs]
results_path: Optional[str] = field(
default=None, metadata={"help": "The path of inference results."}
)
[docs]
def __post_init__(self):
if self.save_results:
if self.results_path is None:
raise ValueError("Need to specify results_path when save_results is True.")
else:
if not self.results_path.endswith(".json"):
raise ValueError("The results_path must be a json file.")
else:
Path(self.results_path).parent.mkdir(parents=True, exist_ok=True)
@dataclass
[docs]
class RaftAlignerArguments(TrainingArguments):
"""
Define a class RaftAlignerArguments to configure raft aligner.
"""
[docs]
output_reward_path: Optional[str] = field(
default="tmp/raft_aligner/",
metadata={
"help": "The path of output rewards."
}
)
[docs]
output_min_length: Optional[int] = field(
default=64,
metadata={
"help": (
"minimum length of the output token sequence generated from"
" model given an input."
),
},
)
[docs]
output_max_length: Optional[int] = field(
default=128,
metadata={
"help": (
"maximum length of the output token sequence generated from"
" model given an output."
),
},
)
[docs]
num_raft_iteration: Optional[int] = field(
default=20,
metadata={
"help": "number of iterations of the raft aligner."
},
)
[docs]
raft_batch_size: Optional[int] = field(
default=1024,
metadata={
"help": (
"only select {raft_batch_size} samples each time for STF training."
)
},
)
[docs]
top_reward_percentage: Optional[float] = field(
default=0.2,
metadata={
"help": (
"only top {top_reward_percentage} samples in the raft batch,"
" (in terms of rewards), will be used for SFT the model."
),
},
)
[docs]
inference_batch_size_per_device: Optional[int] = field(
default=1,
metadata={
"help": (
"every device will infer {inference_batch_size_per_device}"
" samples in parallel. The inferred results will be concatenaed"
" with inputs and attach a reward."
),
},
)
[docs]
collection_strategy: Optional[str] = field(
default="top",
metadata={
"help": (
"{collection_strategy} is either top or local"
" top means that we rank the samples globally regardless of the prompts"
" local means that we only rank the samples with the same prompt"
),
},
)
@dataclass
[docs]
class BenchmarkingArguments:
[docs]
dataset_name: Optional[str] = field(
default=None,
metadata={
"help": "benchmark dataset name provided by lmflow"
},
)
[docs]
lm_evaluation_metric: Optional[str] = field(
default="accuracy",
metadata={
"help": "the metric the model will be evaluated on",
"choices": ["acc", "acc_norm", "bleu", "chrf", "em", "f1", "ppl", \
"ter", "r@1", "r@2", "mrr", "mc1", "mc2", "word_perplexity", \
"byte_perplexity", "bits_per_byte"],
},
)
@dataclass
[docs]
class DPOAlignerArguments:
"""
The arguments for the DPO training script.
"""
[docs]
local_rank: int = field(
default=-1,
metadata={"help": "For distributed training: local_rank"
},
)
# data parameters
[docs]
beta: Optional[float] = field(
default=0.1,
metadata={
"help": "the beta parameter for DPO loss"
}
)
# # training parameters
[docs]
learning_rate: Optional[float] = field(
default=5e-4,
metadata={
"help": "optimizer learning rate"
}
)
[docs]
lr_scheduler_type: Optional[str] = field(
default="cosine",
metadata={
"help": "the lr scheduler type"
}
)
[docs]
warmup_steps: Optional[int] = field(
default=100, metadata={
"help": "the number of warmup steps"
}
)
[docs]
weight_decay: Optional[float] = field(
default=0.05, metadata={
"help": "the weight decay"
}
)
[docs]
optimizer_type: Optional[str] = field(
default="paged_adamw_32bit",
metadata={
"help": "the optimizer type"
}
)
[docs]
per_device_train_batch_size: Optional[int] = field(
default=4,
metadata={
"help": "train batch size per device"
}
)
[docs]
per_device_eval_batch_size: Optional[int] = field(
default=1, metadata={
"help": "eval batch size per device"
}
)
[docs]
gradient_accumulation_steps: Optional[int] = field(
default=4,
metadata={
"help": "the number of gradient accumulation steps"
},
)
[docs]
gradient_checkpointing: Optional[bool] = field(
default=True,
metadata={
"help": "whether to use gradient checkpointing"
},
)
[docs]
gradient_checkpointing_use_reentrant: Optional[bool] = field(
default=False,
metadata={
"help": "whether to use reentrant for gradient checkpointing"
},
)
[docs]
max_prompt_length: Optional[int] = field(
default=512,
metadata={
"help": "the maximum prompt length"
},
)
[docs]
max_length: Optional[int] = field(
default=1024,
metadata={
"help": "the maximum sequence length"
},
)
[docs]
max_steps: Optional[int] = field(
default=1000,
metadata={
"help": "max number of training steps"
},
)
[docs]
logging_steps: Optional[int] = field(
default=10,
metadata={
"help": "the logging frequency"
},
)
[docs]
save_steps: Optional[int] = field(
default=100,
metadata={
"help": "the saving frequency"
},
)
[docs]
eval_steps: Optional[int] = field(
default=100,
metadata={
"help": "the evaluation frequency"
},
)
[docs]
output_dir: Optional[str] = field(
default="./results",
metadata={
"help": "the output directory"
},
)
[docs]
log_freq: Optional[int] = field(
default=1,
metadata={
"help": "the logging frequency"
},
)
[docs]
sanity_check: Optional[bool] = field(
default=False,
metadata={
"help": "only train on 1000 samples"
}
)
[docs]
report_to: Optional[str] = field(
default="wandb",
metadata={
"help": 'The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,'
'`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. '
'Use `"all"` to report to all integrations installed, `"none"` for no integrations.'
},
)
[docs]
seed: Optional[int] = field(
default=0, metadata={"help": "Random seed that will be set at the beginning of training."}
)
[docs]
run_name: Optional[str] = field(
default="dpo", metadata={"help": "The name of the run."}
)
[docs]
eval_dataset_path: Optional[str] = field(
default=None, metadata={"help": "The path of the eval dataset."}
)
@dataclass
[docs]
class DPOv2AlignerArguments(FinetunerArguments):
"""
The arguments for the DPOv2 training script.
"""
# general args
[docs]
random_seed: Optional[int] = field(default=42, metadata={"help": "the random seed"})
[docs]
accelerate_config_file: Optional[str] = field(
default=None,
metadata={"help": "file path for accelerate config file, only used in memory safe dpov2 align."}
)
# pair sampling args
[docs]
margin_scale: Optional[float] = field(default=1.0, metadata={"help": "the margin scale"})
[docs]
sampling_paired_method: Optional[str] = field(default="max_random", metadata={"help": "the choose type"})
[docs]
length_penalty: Optional[float] = field(default=0, metadata={"help": "the length penalty"})
# data collator args
[docs]
max_length: Optional[int] = field(default=2048, metadata={"help": "the maximum sequence length, prompt + output"})
[docs]
max_prompt_length: Optional[int] = field(default=1000, metadata={"help": "the maximum prompt length"})
[docs]
mask_prompt: Optional[bool] = field(default=False, metadata={"help": "mask prompt"})
# dpov2 aligner args
[docs]
beta: Optional[float] = field(default=0.1, metadata={"help": "the beta parameter for DPO loss"})
[docs]
loss_type: Optional[str] = field(default="sigmoid", metadata={"help": "the loss type"})
@dataclass
[docs]
class IterativeAlignerArguments(InferencerArguments):
"""
Arguments for iterative aligners.
"""
[docs]
dataset_path_list: List[str] = field(
default_factory=list,
metadata={"help": "The list of dataset paths for iterative aligners."}
)
[docs]
initial_iter_idx: int = field(
default=0,
metadata={"help": "The initial iteration index, 0 refers to the first dataset in dataset_path_list."}
)
@dataclass
[docs]
class IterativeDPOAlignerArguments(IterativeAlignerArguments, DPOv2AlignerArguments):
"""
Arguments for iterative DPO aligners.
"""
[docs]
output_dir: Optional[str] = field(
default="./runs",
metadata={"help": "Output path for the inferenced results"},
)
[docs]
reward_model_inference_batch_size: int = field(
default=1,
metadata={"help": "The batch size for reward model inference."}
)
[docs]
reward_model_inference_block_size: int = field(
default=2048,
metadata={"help": "The block size for reward model inference."}
)
[docs]
do_response_generation: bool = field(
default=True,
metadata={"help": "Whether to generate responses using the model."}
)
[docs]
do_scoring: bool = field(
default=True,
metadata={"help": "Whether to score the responses using the reward model."}
)
[docs]
do_dpo_align: bool = field(
default=True,
metadata={"help": "Whether to perform DPO alignment."}
)
[docs]
PIPELINE_ARGUMENT_MAPPING = {
"finetuner": FinetunerArguments,
"evaluator": EvaluatorArguments,
"inferencer": InferencerArguments,
"vllm_inferencer": InferencerArguments,
"rm_inferencer": InferencerArguments,
"raft_aligner": RaftAlignerArguments,
"dpo_aligner": DPOAlignerArguments,
"rm_tuner": RewardModelTunerArguments,
"dpov2_aligner": DPOv2AlignerArguments,
"iterative_dpo_aligner": IterativeDPOAlignerArguments,
}
[docs]
class AutoArguments:
"""
Automatically choose arguments from FinetunerArguments or EvaluatorArguments.
"""
[docs]
def get_pipeline_args_class(pipeline_name: str):
return PIPELINE_ARGUMENT_MAPPING[pipeline_name]
