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import os
from dataclasses import dataclass, field
from typing import Any, Dict, List, Tuple
import llm_studio.src.datasets.text_causal_regression_ds
import llm_studio.src.plots.text_causal_classification_modeling_plots
from llm_studio.app_utils.config import default_cfg
from llm_studio.python_configs.base import DefaultConfig, DefaultConfigProblemBase
from llm_studio.python_configs.text_causal_classification_modeling_config import (
ConfigNLPCausalClassificationAugmentation as ConfigNLPCausalRegressionAugmentation,
)
from llm_studio.python_configs.text_causal_classification_modeling_config import (
ConfigNLPCausalClassificationDataset,
)
from llm_studio.python_configs.text_causal_classification_modeling_config import (
ConfigNLPCausalClassificationLogging as ConfigNLPCausalRegressionLogging,
)
from llm_studio.python_configs.text_causal_classification_modeling_config import (
ConfigNLPCausalClassificationTokenizer as ConfigNLPCausalRegressionTokenizer,
)
from llm_studio.python_configs.text_causal_classification_modeling_config import (
ConfigNLPCausalClassificationTraining,
)
from llm_studio.python_configs.text_causal_language_modeling_config import (
ConfigNLPCausalLMArchitecture,
ConfigNLPCausalLMEnvironment,
)
from llm_studio.src import possible_values
from llm_studio.src.losses import text_causal_regression_modeling_losses
from llm_studio.src.metrics import text_causal_regression_modeling_metrics
from llm_studio.src.models import text_causal_regression_modeling_model
from llm_studio.src.utils.modeling_utils import generate_experiment_name
@dataclass
class ConfigNLPCausalRegressionDataset(ConfigNLPCausalClassificationDataset):
dataset_class: Any = llm_studio.src.datasets.text_causal_regression_ds.CustomDataset
num_classes: int = 1
def __post_init__(self):
self.prompt_column = (
tuple(
self.prompt_column,
)
if isinstance(self.prompt_column, str)
else tuple(self.prompt_column)
)
super().__post_init__()
self._visibility["num_classes"] = -1
@dataclass
class ConfigNLPCausalRegressionTraining(ConfigNLPCausalClassificationTraining):
loss_class: Any = text_causal_regression_modeling_losses.Losses
loss_function: str = "MSELoss"
learning_rate: float = 0.0001
differential_learning_rate_layers: Tuple[str, ...] = ("regression_head",)
differential_learning_rate: float = 0.00001
def __post_init__(self):
super().__post_init__()
self._possible_values["loss_function"] = self.loss_class.names()
self._possible_values["differential_learning_rate_layers"] = (
possible_values.String(
values=("backbone", "embed", "regression_head"),
allow_custom=False,
placeholder="Select optional layers...",
)
)
@dataclass
class ConfigNLPCausalRegressionArchitecture(ConfigNLPCausalLMArchitecture):
model_class: Any = text_causal_regression_modeling_model.Model
def __post_init__(self):
super().__post_init__()
@dataclass
class ConfigNLPCausalRegressionPrediction(DefaultConfig):
metric_class: Any = text_causal_regression_modeling_metrics.Metrics
metric: str = "MSE"
batch_size_inference: int = 0
def __post_init__(self):
super().__post_init__()
self._possible_values["metric"] = self.metric_class.names()
self._possible_values["batch_size_inference"] = (0, 512, 1)
self._visibility["metric_class"] = -1
@dataclass
class ConfigNLPCausalRegressionEnvironment(ConfigNLPCausalLMEnvironment):
_model_card_template: str = "text_causal_regression_model_card_template.md"
_summary_card_template: str = (
"text_causal_regression_experiment_summary_card_template.md"
)
def __post_init__(self):
super().__post_init__()
@dataclass
class ConfigProblemBase(DefaultConfigProblemBase):
output_directory: str = f"output/{os.path.basename(__file__).split('.')[0]}"
experiment_name: str = field(default_factory=generate_experiment_name)
llm_backbone: str = (
"h2oai/h2o-danube3-500m-chat"
if "h2oai/h2o-danube3-500m-chat" in default_cfg.default_causal_language_models
else default_cfg.default_causal_language_models[0]
)
dataset: ConfigNLPCausalRegressionDataset = field(
default_factory=ConfigNLPCausalRegressionDataset
)
tokenizer: ConfigNLPCausalRegressionTokenizer = field(
default_factory=ConfigNLPCausalRegressionTokenizer
)
architecture: ConfigNLPCausalRegressionArchitecture = field(
default_factory=ConfigNLPCausalRegressionArchitecture
)
training: ConfigNLPCausalRegressionTraining = field(
default_factory=ConfigNLPCausalRegressionTraining
)
augmentation: ConfigNLPCausalRegressionAugmentation = field(
default_factory=ConfigNLPCausalRegressionAugmentation
)
prediction: ConfigNLPCausalRegressionPrediction = field(
default_factory=ConfigNLPCausalRegressionPrediction
)
environment: ConfigNLPCausalRegressionEnvironment = field(
default_factory=ConfigNLPCausalRegressionEnvironment
)
logging: ConfigNLPCausalRegressionLogging = field(
default_factory=ConfigNLPCausalRegressionLogging
)
def __post_init__(self):
super().__post_init__()
self._visibility["output_directory"] = -1
self._possible_values["llm_backbone"] = possible_values.String(
values=default_cfg.default_causal_language_models,
allow_custom=True,
)
def check(self) -> Dict[str, List]:
errors: Dict[str, List] = {"title": [], "message": [], "type": []}
if isinstance(self.dataset.answer_column, str):
errors["title"].append("Invalid answer_column type")
errors["message"].append(
"Providing the answer_column as a string is deprecated. "
"Please provide the answer_column as a list."
)
errors["type"].append("deprecated")
self.dataset.answer_column = [self.dataset.answer_column]
if self.dataset.parent_id_column not in ["None", None]:
errors["title"] += ["Parent ID column is not supported for regression"]
errors["message"] += [
"Parent ID column is not supported for regression datasets."
]
errors["type"].append("error")
return errors
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