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import json
from dataclasses import dataclass
from typing import Optional
from autotrain.trainers.clm.params import LLMTrainingParams
from autotrain.trainers.extractive_question_answering.params import ExtractiveQuestionAnsweringParams
from autotrain.trainers.image_classification.params import ImageClassificationParams
from autotrain.trainers.image_regression.params import ImageRegressionParams
from autotrain.trainers.object_detection.params import ObjectDetectionParams
from autotrain.trainers.sent_transformers.params import SentenceTransformersParams
from autotrain.trainers.seq2seq.params import Seq2SeqParams
from autotrain.trainers.tabular.params import TabularParams
from autotrain.trainers.text_classification.params import TextClassificationParams
from autotrain.trainers.text_regression.params import TextRegressionParams
from autotrain.trainers.token_classification.params import TokenClassificationParams
from autotrain.trainers.vlm.params import VLMTrainingParams
HIDDEN_PARAMS = [
"token",
"project_name",
"username",
"task",
"backend",
"train_split",
"valid_split",
"text_column",
"rejected_text_column",
"prompt_text_column",
"push_to_hub",
"trainer",
"model",
"data_path",
"image_path",
"class_image_path",
"revision",
"tokenizer",
"class_prompt",
"num_class_images",
"class_labels_conditioning",
"resume_from_checkpoint",
"dataloader_num_workers",
"allow_tf32",
"prior_generation_precision",
"local_rank",
"tokenizer_max_length",
"rank",
"xl",
"checkpoints_total_limit",
"validation_images",
"validation_epochs",
"num_validation_images",
"validation_prompt",
"sample_batch_size",
"log",
"image_column",
"target_column",
"id_column",
"target_columns",
"tokens_column",
"tags_column",
"objects_column",
"sentence1_column",
"sentence2_column",
"sentence3_column",
"question_column",
"answer_column",
]
PARAMS = {}
PARAMS["llm"] = LLMTrainingParams(
target_modules="all-linear",
log="tensorboard",
mixed_precision="fp16",
quantization="int4",
peft=True,
block_size=1024,
epochs=3,
padding="right",
chat_template="none",
max_completion_length=128,
distributed_backend="ddp",
).model_dump()
PARAMS["text-classification"] = TextClassificationParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["st"] = SentenceTransformersParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["image-classification"] = ImageClassificationParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["image-object-detection"] = ObjectDetectionParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["seq2seq"] = Seq2SeqParams(
mixed_precision="fp16",
target_modules="all-linear",
log="tensorboard",
).model_dump()
PARAMS["tabular"] = TabularParams(
categorical_imputer="most_frequent",
numerical_imputer="median",
numeric_scaler="robust",
).model_dump()
PARAMS["token-classification"] = TokenClassificationParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["text-regression"] = TextRegressionParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["image-regression"] = ImageRegressionParams(
mixed_precision="fp16",
log="tensorboard",
).model_dump()
PARAMS["vlm"] = VLMTrainingParams(
mixed_precision="fp16",
target_modules="all-linear",
log="tensorboard",
quantization="int4",
peft=True,
epochs=3,
).model_dump()
PARAMS["extractive-qa"] = ExtractiveQuestionAnsweringParams(
mixed_precision="fp16",
log="tensorboard",
max_seq_length=512,
max_doc_stride=128,
).model_dump()
@dataclass
class AppParams:
"""
AppParams class is responsible for managing and processing parameters for various machine learning tasks.
Attributes:
job_params_json (str): JSON string containing job parameters.
token (str): Authentication token.
project_name (str): Name of the project.
username (str): Username of the project owner.
task (str): Type of task to be performed.
data_path (str): Path to the dataset.
base_model (str): Base model to be used.
column_mapping (dict): Mapping of columns for the dataset.
train_split (Optional[str]): Name of the training split. Default is None.
valid_split (Optional[str]): Name of the validation split. Default is None.
using_hub_dataset (Optional[bool]): Flag indicating if a hub dataset is used. Default is False.
api (Optional[bool]): Flag indicating if API is used. Default is False.
Methods:
__post_init__(): Validates the parameters after initialization.
munge(): Processes the parameters based on the task type.
_munge_common_params(): Processes common parameters for all tasks.
_munge_params_sent_transformers(): Processes parameters for sentence transformers task.
_munge_params_llm(): Processes parameters for large language model task.
_munge_params_vlm(): Processes parameters for vision-language model task.
_munge_params_text_clf(): Processes parameters for text classification task.
_munge_params_extractive_qa(): Processes parameters for extractive question answering task.
_munge_params_text_reg(): Processes parameters for text regression task.
_munge_params_token_clf(): Processes parameters for token classification task.
_munge_params_seq2seq(): Processes parameters for sequence-to-sequence task.
_munge_params_img_clf(): Processes parameters for image classification task.
_munge_params_img_reg(): Processes parameters for image regression task.
_munge_params_img_obj_det(): Processes parameters for image object detection task.
_munge_params_tabular(): Processes parameters for tabular data task.
"""
job_params_json: str
token: str
project_name: str
username: str
task: str
data_path: str
base_model: str
column_mapping: dict
train_split: Optional[str] = None
valid_split: Optional[str] = None
using_hub_dataset: Optional[bool] = False
api: Optional[bool] = False
def __post_init__(self):
if self.using_hub_dataset and not self.train_split:
raise ValueError("train_split is required when using a hub dataset")
def munge(self):
if self.task == "text-classification":
return self._munge_params_text_clf()
elif self.task == "seq2seq":
return self._munge_params_seq2seq()
elif self.task == "image-classification":
return self._munge_params_img_clf()
elif self.task == "image-object-detection":
return self._munge_params_img_obj_det()
elif self.task.startswith("tabular"):
return self._munge_params_tabular()
elif self.task.startswith("llm"):
return self._munge_params_llm()
elif self.task == "token-classification":
return self._munge_params_token_clf()
elif self.task == "text-regression":
return self._munge_params_text_reg()
elif self.task.startswith("st:"):
return self._munge_params_sent_transformers()
elif self.task == "image-regression":
return self._munge_params_img_reg()
elif self.task.startswith("vlm"):
return self._munge_params_vlm()
elif self.task == "extractive-qa":
return self._munge_params_extractive_qa()
else:
raise ValueError(f"Unknown task: {self.task}")
def _munge_common_params(self):
_params = json.loads(self.job_params_json)
_params["token"] = self.token
_params["project_name"] = f"{self.project_name}"
if "push_to_hub" not in _params:
_params["push_to_hub"] = True
_params["data_path"] = self.data_path
_params["username"] = self.username
return _params
def _munge_params_sent_transformers(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["sentence1_column"] = "autotrain_sentence1"
_params["sentence2_column"] = "autotrain_sentence2"
_params["sentence3_column"] = "autotrain_sentence3"
_params["target_column"] = "autotrain_target"
_params["valid_split"] = "validation"
else:
_params["sentence1_column"] = self.column_mapping.get(
"sentence1" if not self.api else "sentence1_column", "sentence1"
)
_params["sentence2_column"] = self.column_mapping.get(
"sentence2" if not self.api else "sentence2_column", "sentence2"
)
_params["sentence3_column"] = self.column_mapping.get(
"sentence3" if not self.api else "sentence3_column", "sentence3"
)
_params["target_column"] = self.column_mapping.get("target" if not self.api else "target_column", "target")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
trainer = self.task.split(":")[1]
_params["trainer"] = trainer.lower()
return SentenceTransformersParams(**_params)
def _munge_params_llm(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["prompt_text_column"] = "autotrain_prompt"
_params["rejected_text_column"] = "autotrain_rejected_text"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["prompt_text_column"] = self.column_mapping.get(
"prompt" if not self.api else "prompt_text_column", "prompt"
)
_params["rejected_text_column"] = self.column_mapping.get(
"rejected_text" if not self.api else "rejected_text_column", "rejected_text"
)
_params["train_split"] = self.train_split
if "log" not in _params:
_params["log"] = "tensorboard"
trainer = self.task.split(":")[1]
if trainer != "generic":
_params["trainer"] = trainer.lower()
if "quantization" in _params:
if _params["quantization"] in ("none", "no"):
_params["quantization"] = None
return LLMTrainingParams(**_params)
def _munge_params_vlm(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["prompt_text_column"] = "autotrain_prompt"
_params["image_column"] = "autotrain_image"
_params["valid_split"] = "validation"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["prompt_text_column"] = self.column_mapping.get(
"prompt" if not self.api else "prompt_text_column", "prompt"
)
_params["image_column"] = self.column_mapping.get(
"image" if not self.api else "rejected_text_column", "image"
)
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
if "log" not in _params:
_params["log"] = "tensorboard"
trainer = self.task.split(":")[1]
_params["trainer"] = trainer.lower()
if "quantization" in _params:
if _params["quantization"] in ("none", "no"):
_params["quantization"] = None
return VLMTrainingParams(**_params)
def _munge_params_text_clf(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["target_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["target_column"] = self.column_mapping.get("label" if not self.api else "target_column", "label")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return TextClassificationParams(**_params)
def _munge_params_extractive_qa(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["question_column"] = "autotrain_question"
_params["answer_column"] = "autotrain_answer"
_params["valid_split"] = "validation"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["question_column"] = self.column_mapping.get(
"question" if not self.api else "question_column", "question"
)
_params["answer_column"] = self.column_mapping.get("answer" if not self.api else "answer_column", "answer")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return ExtractiveQuestionAnsweringParams(**_params)
def _munge_params_text_reg(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["target_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["target_column"] = self.column_mapping.get("label" if not self.api else "target_column", "label")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return TextRegressionParams(**_params)
def _munge_params_token_clf(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["tokens_column"] = "autotrain_text"
_params["tags_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["tokens_column"] = self.column_mapping.get("tokens" if not self.api else "tokens_column", "tokens")
_params["tags_column"] = self.column_mapping.get("tags" if not self.api else "tags_column", "tags")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return TokenClassificationParams(**_params)
def _munge_params_seq2seq(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["text_column"] = "autotrain_text"
_params["target_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["text_column"] = self.column_mapping.get("text" if not self.api else "text_column", "text")
_params["target_column"] = self.column_mapping.get("label" if not self.api else "target_column", "label")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return Seq2SeqParams(**_params)
def _munge_params_img_clf(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["image_column"] = "autotrain_image"
_params["target_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["image_column"] = self.column_mapping.get("image" if not self.api else "image_column", "image")
_params["target_column"] = self.column_mapping.get("label" if not self.api else "target_column", "label")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return ImageClassificationParams(**_params)
def _munge_params_img_reg(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["image_column"] = "autotrain_image"
_params["target_column"] = "autotrain_label"
_params["valid_split"] = "validation"
else:
_params["image_column"] = self.column_mapping.get("image" if not self.api else "image_column", "image")
_params["target_column"] = self.column_mapping.get("target" if not self.api else "target_column", "target")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return ImageRegressionParams(**_params)
def _munge_params_img_obj_det(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if "log" not in _params:
_params["log"] = "tensorboard"
if not self.using_hub_dataset:
_params["image_column"] = "autotrain_image"
_params["objects_column"] = "autotrain_objects"
_params["valid_split"] = "validation"
else:
_params["image_column"] = self.column_mapping.get("image" if not self.api else "image_column", "image")
_params["objects_column"] = self.column_mapping.get(
"objects" if not self.api else "objects_column", "objects"
)
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
return ObjectDetectionParams(**_params)
def _munge_params_tabular(self):
_params = self._munge_common_params()
_params["model"] = self.base_model
if not self.using_hub_dataset:
_params["id_column"] = "autotrain_id"
_params["valid_split"] = "validation"
if len(self.column_mapping["label"]) == 1:
_params["target_columns"] = ["autotrain_label"]
else:
_params["target_columns"] = [
"autotrain_label_" + str(i) for i in range(len(self.column_mapping["label"]))
]
else:
_params["id_column"] = self.column_mapping.get("id" if not self.api else "id_column", "id")
_params["train_split"] = self.train_split
_params["valid_split"] = self.valid_split
_params["target_columns"] = self.column_mapping.get("label" if not self.api else "target_columns", "label")
if len(_params["categorical_imputer"].strip()) == 0 or _params["categorical_imputer"].lower() == "none":
_params["categorical_imputer"] = None
if len(_params["numerical_imputer"].strip()) == 0 or _params["numerical_imputer"].lower() == "none":
_params["numerical_imputer"] = None
if len(_params["numeric_scaler"].strip()) == 0 or _params["numeric_scaler"].lower() == "none":
_params["numeric_scaler"] = None
if "classification" in self.task:
_params["task"] = "classification"
else:
_params["task"] = "regression"
return TabularParams(**_params)
def get_task_params(task, param_type):
"""
Retrieve task-specific parameters while filtering out hidden parameters based on the task and parameter type.
Args:
task (str): The task identifier, which can include prefixes like "llm", "st:", "vlm:", etc.
param_type (str): The type of parameters to retrieve, typically "basic" or other types.
Returns:
dict: A dictionary of task-specific parameters with hidden parameters filtered out.
Notes:
- The function handles various task prefixes and adjusts the task and trainer variables accordingly.
- Hidden parameters are filtered out based on the task and parameter type.
- Additional hidden parameters are defined for specific tasks and trainers.
"""
if task.startswith("llm"):
trainer = task.split(":")[1].lower()
task = task.split(":")[0].lower()
if task.startswith("st:"):
trainer = task.split(":")[1].lower()
task = task.split(":")[0].lower()
if task.startswith("vlm:"):
trainer = task.split(":")[1].lower()
task = task.split(":")[0].lower()
if task.startswith("tabular"):
task = "tabular"
if task not in PARAMS:
return {}
task_params = PARAMS[task]
task_params = {k: v for k, v in task_params.items() if k not in HIDDEN_PARAMS}
if task == "llm":
more_hidden_params = []
if trainer == "sft":
more_hidden_params = [
"model_ref",
"dpo_beta",
"add_eos_token",
"max_prompt_length",
"max_completion_length",
]
elif trainer == "reward":
more_hidden_params = [
"model_ref",
"dpo_beta",
"add_eos_token",
"max_prompt_length",
"max_completion_length",
"unsloth",
]
elif trainer == "orpo":
more_hidden_params = [
"model_ref",
"dpo_beta",
"add_eos_token",
"unsloth",
]
elif trainer == "generic":
more_hidden_params = [
"model_ref",
"dpo_beta",
"max_prompt_length",
"max_completion_length",
]
elif trainer == "dpo":
more_hidden_params = [
"add_eos_token",
"unsloth",
]
if param_type == "basic":
more_hidden_params.extend(
[
"padding",
"use_flash_attention_2",
"disable_gradient_checkpointing",
"logging_steps",
"eval_strategy",
"save_total_limit",
"auto_find_batch_size",
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"quantization",
"merge_adapter",
"lora_r",
"lora_alpha",
"lora_dropout",
"max_completion_length",
]
)
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "text-classification" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "extractive-qa" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "st" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "vlm" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
"quantization",
"lora_r",
"lora_alpha",
"lora_dropout",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "text-regression" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "image-classification" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "image-regression" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "image-object-detection" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "seq2seq" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"quantization",
"lora_r",
"lora_alpha",
"lora_dropout",
"target_modules",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
if task == "token-classification" and param_type == "basic":
more_hidden_params = [
"warmup_ratio",
"weight_decay",
"max_grad_norm",
"seed",
"logging_steps",
"auto_find_batch_size",
"save_total_limit",
"eval_strategy",
"early_stopping_patience",
"early_stopping_threshold",
]
task_params = {k: v for k, v in task_params.items() if k not in more_hidden_params}
return task_params
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