File size: 7,962 Bytes
07423df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
import os
from dataclasses import dataclass, field
from typing import Any, Dict, List, Tuple
import llm_studio.src.datasets.text_causal_classification_ds
import llm_studio.src.plots.text_causal_classification_modeling_plots
from llm_studio.python_configs.base import DefaultConfig, DefaultConfigProblemBase
from llm_studio.python_configs.text_causal_language_modeling_config import (
ConfigNLPAugmentation,
ConfigNLPCausalLMArchitecture,
ConfigNLPCausalLMDataset,
ConfigNLPCausalLMEnvironment,
ConfigNLPCausalLMLogging,
ConfigNLPCausalLMTokenizer,
ConfigNLPCausalLMTraining,
)
from llm_studio.src import possible_values
from llm_studio.src.losses import text_causal_classification_modeling_losses
from llm_studio.src.metrics import text_causal_classification_modeling_metrics
from llm_studio.src.models import text_causal_classification_modeling_model
from llm_studio.src.utils.modeling_utils import generate_experiment_name
@dataclass
class ConfigNLPCausalClassificationDataset(ConfigNLPCausalLMDataset):
dataset_class: Any = (
llm_studio.src.datasets.text_causal_classification_ds.CustomDataset
)
system_column: str = "None"
prompt_column: Tuple[str, ...] = ("instruction", "input")
answer_column: str = "label"
num_classes: int = 1
parent_id_column: str = "None"
text_system_start: str = ""
text_prompt_start: str = ""
text_answer_separator: str = ""
add_eos_token_to_system: bool = False
add_eos_token_to_prompt: bool = False
add_eos_token_to_answer: bool = False
_allowed_file_extensions: Tuple[str, ...] = ("csv", "pq", "parquet")
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._possible_values["num_classes"] = (1, 100, 1)
self._visibility["personalize"] = -1
self._visibility["chatbot_name"] = -1
self._visibility["chatbot_author"] = -1
self._visibility["mask_prompt_labels"] = -1
self._visibility["add_eos_token_to_answer"] = -1
@dataclass
class ConfigNLPCausalClassificationTraining(ConfigNLPCausalLMTraining):
loss_class: Any = text_causal_classification_modeling_losses.Losses
loss_function: str = "BinaryCrossEntropyLoss"
learning_rate: float = 0.0001
differential_learning_rate_layers: Tuple[str, ...] = ("classification_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", "classification_head"),
allow_custom=False,
placeholder="Select optional layers...",
)
)
@dataclass
class ConfigNLPCausalClassificationTokenizer(ConfigNLPCausalLMTokenizer):
max_length_prompt: int = 512
max_length: int = 512
def __post_init__(self):
super().__post_init__()
self._visibility["max_length_answer"] = -1
@dataclass
class ConfigNLPCausalClassificationArchitecture(ConfigNLPCausalLMArchitecture):
model_class: Any = text_causal_classification_modeling_model.Model
def __post_init__(self):
super().__post_init__()
@dataclass
class ConfigNLPCausalClassificationPrediction(DefaultConfig):
metric_class: Any = text_causal_classification_modeling_metrics.Metrics
metric: str = "AUC"
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 ConfigNLPCausalClassificationEnvironment(ConfigNLPCausalLMEnvironment):
_model_card_template: str = "text_causal_classification_model_card_template.md"
_summary_card_template: str = (
"text_causal_classification_experiment_summary_card_template.md"
)
def __post_init__(self):
super().__post_init__()
@dataclass
class ConfigNLPCausalClassificationLogging(ConfigNLPCausalLMLogging):
plots_class: Any = (
llm_studio.src.plots.text_causal_classification_modeling_plots.Plots
)
@dataclass
class ConfigProblemBase(DefaultConfigProblemBase):
output_directory: str = f"output/{os.path.basename(__file__).split('.')[0]}"
experiment_name: str = field(default_factory=generate_experiment_name)
_parent_experiment: str = ""
llm_backbone: str = "h2oai/h2ogpt-4096-llama2-7b"
dataset: ConfigNLPCausalClassificationDataset = field(
default_factory=ConfigNLPCausalClassificationDataset
)
tokenizer: ConfigNLPCausalLMTokenizer = field(
default_factory=ConfigNLPCausalLMTokenizer
)
architecture: ConfigNLPCausalClassificationArchitecture = field(
default_factory=ConfigNLPCausalClassificationArchitecture
)
training: ConfigNLPCausalClassificationTraining = field(
default_factory=ConfigNLPCausalClassificationTraining
)
augmentation: ConfigNLPAugmentation = field(default_factory=ConfigNLPAugmentation)
prediction: ConfigNLPCausalClassificationPrediction = field(
default_factory=ConfigNLPCausalClassificationPrediction
)
environment: ConfigNLPCausalClassificationEnvironment = field(
default_factory=ConfigNLPCausalClassificationEnvironment
)
logging: ConfigNLPCausalClassificationLogging = field(
default_factory=ConfigNLPCausalClassificationLogging
)
def __post_init__(self):
super().__post_init__()
self._visibility["output_directory"] = -1
self._possible_values["llm_backbone"] = possible_values.String(
values=(
"h2oai/h2o-danube2-1.8b-base",
"h2oai/h2o-danube2-1.8b-chat",
"h2oai/h2ogpt-4096-llama2-7b",
"h2oai/h2ogpt-4096-llama2-7b-chat",
"h2oai/h2ogpt-4096-llama2-13b",
"h2oai/h2ogpt-4096-llama2-13b-chat",
"h2oai/h2ogpt-4096-llama2-70b",
"h2oai/h2ogpt-4096-llama2-70b-chat",
"tiiuae/falcon-7b",
"mistralai/Mistral-7B-v0.1",
"HuggingFaceH4/zephyr-7b-beta",
"google/gemma-2b",
"google/gemma-7b",
"stabilityai/stablelm-3b-4e1t",
"microsoft/phi-2",
"facebook/opt-125m",
),
allow_custom=True,
)
def check(self) -> Dict[str, List]:
errors: Dict[str, List] = {"title": [], "message": []}
if self.training.loss_function == "CrossEntropyLoss":
if self.dataset.num_classes == 1:
errors["title"] += ["CrossEntropyLoss requires num_classes > 1"]
errors["message"] += [
"CrossEntropyLoss requires num_classes > 1, "
"but num_classes is set to 1."
]
elif self.training.loss_function == "BinaryCrossEntropyLoss":
if self.dataset.num_classes != 1:
errors["title"] += ["BinaryCrossEntropyLoss requires num_classes == 1"]
errors["message"] += [
"BinaryCrossEntropyLoss requires num_classes == 1, "
"but num_classes is set to {}.".format(self.dataset.num_classes)
]
if self.dataset.parent_id_column not in ["None", None]:
errors["title"] += ["Parent ID column is not supported for classification"]
errors["message"] += [
"Parent ID column is not supported for classification datasets."
]
return errors
|