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import multiprocessing
import os
from dataclasses import dataclass, field
from typing import Any, Dict, List, Tuple
import torch
import llm_studio.src.datasets.text_causal_language_modeling_ds
from llm_studio.python_configs.base import DefaultConfig, DefaultConfigProblemBase
from llm_studio.src import possible_values
from llm_studio.src.augmentations.nlp_aug import BaseNLPAug
from llm_studio.src.loggers import Loggers
from llm_studio.src.losses import text_causal_language_modeling_losses
from llm_studio.src.metrics import text_causal_language_modeling_metrics
from llm_studio.src.models import text_causal_language_modeling_model
from llm_studio.src.nesting import Dependency
from llm_studio.src.optimizers import Optimizers
from llm_studio.src.plots import text_causal_language_modeling_plots
from llm_studio.src.schedulers import Schedulers
from llm_studio.src.utils.modeling_utils import generate_experiment_name
@dataclass
class ConfigNLPCausalLMDataset(DefaultConfig):
dataset_class: Any = (
llm_studio.src.datasets.text_causal_language_modeling_ds.CustomDataset
)
personalize: bool = False
chatbot_name: str = "h2oGPT"
chatbot_author: str = "H2O.ai"
train_dataframe: str = "/path/to/train.csv"
validation_strategy: str = "automatic"
validation_dataframe: str = ""
validation_size: float = 0.01
data_sample: float = 1.0
data_sample_choice: Tuple[str, ...] = ("Train", "Validation")
system_column: str = "None"
prompt_column: Tuple[str, ...] = ("instruction", "input")
answer_column: str = "output"
parent_id_column: str = "None"
text_system_start: str = "<|system|>"
text_prompt_start: str = "<|prompt|>"
text_answer_separator: str = "<|answer|>"
limit_chained_samples: bool = False
add_eos_token_to_system: bool = True
add_eos_token_to_prompt: bool = True
add_eos_token_to_answer: bool = True
mask_prompt_labels: bool = True
_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["train_dataframe"] = possible_values.Files(
prefer_with=lambda path: "train" in path
)
self._possible_values["validation_strategy"] = possible_values.String(
values=(
("custom", "Custom holdout validation"),
("automatic", "Automatic holdout validation"),
),
allow_custom=False,
)
self._possible_values["validation_dataframe"] = possible_values.Files(
add_none=True, prefer_with=lambda path: "val" in path
)
self._possible_values["validation_size"] = (0.01, 0.95, 0.01)
self._possible_values["data_sample"] = (0.01, 1, 0.01)
self._possible_values["data_sample_choice"] = ["Train", "Validation"]
self._possible_values["system_column"] = possible_values.Columns(
prefer_with=lambda column: column in ("system",), add_none=True
)
self._possible_values["prompt_column"] = possible_values.Columns(
prefer_with=lambda column: column in ("instruction", "prompt")
)
self._possible_values["answer_column"] = possible_values.Columns(
prefer_with=lambda column: column in ("answer", "output")
)
self._possible_values["parent_id_column"] = possible_values.Columns(
prefer_with=lambda column: column in ("parent",), add_none=True
)
self._nesting.add(
["chatbot_name", "chatbot_author"],
[Dependency(key="personalize", value=True, is_set=True)],
)
self._nesting.add(
["validation_dataframe"],
[Dependency(key="validation_strategy", value="custom", is_set=True)],
)
self._nesting.add(
["validation_size"],
[Dependency(key="validation_strategy", value="automatic", is_set=True)],
)
self._nesting.add(
["data_sample_choice"],
[Dependency(key="data_sample", value=1, is_set=False)],
)
self._nesting.add(
["limit_chained_samples"],
[Dependency(key="parent_id_column", value="None", is_set=False)],
)
self._nesting.add(
["text_system_start", "add_eos_token_to_system"],
[Dependency(key="system_column", value="None", is_set=False)],
)
self._visibility["dataset_class"] = -1
@dataclass
class ConfigNLPCausalLMTraining(DefaultConfig):
loss_class: Any = text_causal_language_modeling_losses.Losses
loss_function: str = "TokenAveragedCrossEntropy"
optimizer: str = "AdamW"
learning_rate: float = 0.0001
differential_learning_rate_layers: Tuple[str, ...] = ()
differential_learning_rate: float = 0.00001
use_flash_attention_2: bool = False
batch_size: int = 2
drop_last_batch: bool = True
epochs: int = 1
schedule: str = "Cosine"
warmup_epochs: float = 0.0
weight_decay: float = 0.0
gradient_clip: float = 0.0
grad_accumulation: int = 1
lora: bool = True
lora_r: int = 4
lora_alpha: int = 16
lora_dropout: float = 0.05
lora_target_modules: str = ""
save_best_checkpoint: bool = False
evaluation_epochs: float = 1.0
evaluate_before_training: bool = False
train_validation_data: bool = False
def __post_init__(self):
super().__post_init__()
self._possible_values["loss_function"] = self.loss_class.names()
self._possible_values["optimizer"] = Optimizers.names()
self._possible_values["learning_rate"] = possible_values.Number(
step=1e-9, min=1e-9
)
self._possible_values["differential_learning_rate_layers"] = (
possible_values.String(
values=("backbone", "embed"),
allow_custom=False,
placeholder="Select optional layers...",
)
)
self._possible_values["differential_learning_rate"] = self._possible_values[
"learning_rate"
]
self._possible_values["batch_size"] = (1, 256, 1)
self._possible_values["epochs"] = (0, 10, 1)
self._possible_values["schedule"] = Schedulers.names()
self._possible_values["warmup_epochs"] = (0.0, 5, 0.05)
self._possible_values["weight_decay"] = possible_values.Number(step=1e-5, min=0)
self._possible_values["gradient_clip"] = (0.0, 10.0, 0.1)
self._possible_values["grad_accumulation"] = (1, 8, 1)
self._possible_values["lora_r"] = (1, 256, 1)
self._possible_values["lora_alpha"] = (1, 256, 1)
self._possible_values["lora_dropout"] = (0.0, 0.5, 0.01)
self._possible_values["evaluation_epochs"] = (0.01, 1, 0.01)
self._visibility["loss_class"] = -1
self._visibility["drop_last_batch"] = -1
self._visibility["differential_learning_rate_layers"] = 1
self._visibility["differential_learning_rate"] = 1
self._nesting.add(
["differential_learning_rate"],
[
Dependency(
key="differential_learning_rate_layers", value=None, is_set=False
)
],
)
self._nesting.add(
["lora_r", "lora_alpha", "lora_dropout", "lora_target_modules"],
[Dependency(key="lora", value=False, is_set=False)],
)
self._nesting.add(
["train_validation_data"],
[Dependency(key="save_best_checkpoint", value=False, is_set=True)],
)
@dataclass
class ConfigNLPCausalLMTokenizer(DefaultConfig):
max_length_prompt: int = 256
max_length_answer: int = 256
max_length: int = 512
add_prompt_answer_tokens: bool = False
padding_quantile: float = 1.0
use_fast: bool = True
def __post_init__(self):
super().__post_init__()
self._possible_values["max_length_prompt"] = (32, 1024 * 16, 32)
self._possible_values["max_length_answer"] = (32, 1024 * 16, 32)
self._possible_values["max_length"] = (32, 1024 * 16, 32)
self._possible_values["padding_quantile"] = (0, 1, 0.01)
self._padding_side = "left"
@dataclass
class ConfigNLPCausalLMArchitecture(DefaultConfig):
model_class: Any = text_causal_language_modeling_model.Model
pretrained: bool = True
backbone_dtype: str = "int4"
gradient_checkpointing: bool = True
force_embedding_gradients: bool = False
intermediate_dropout: float = 0
pretrained_weights: str = ""
def __post_init__(self):
super().__post_init__()
self._possible_values["backbone_dtype"] = possible_values.String(
values=("float32", "bfloat16", "float16", "int8", "int4"),
allow_custom=False,
)
self._possible_values["intermediate_dropout"] = (0, 0.5, 0.05)
self._nesting.add(
["force_embedding_gradients"],
[Dependency(key="lora", value=False, is_set=False)],
)
self._visibility["model_class"] = -1
self._visibility["pretrained"] = -1
@dataclass
class ConfigNLPAugmentation(DefaultConfig):
nlp_augmentations_class: Any = BaseNLPAug
token_mask_probability: float = 0.0
skip_parent_probability: float = 0.0
random_parent_probability: float = 0.0
neftune_noise_alpha: float = 0.0
def __post_init__(self):
super().__post_init__()
self._possible_values["token_mask_probability"] = (0.0, 0.9, 0.05)
self._possible_values["skip_parent_probability"] = (0.0, 1.0, 0.05)
self._possible_values["random_parent_probability"] = (0.0, 1.0, 0.05)
self._possible_values["neftune_noise_alpha"] = (0.0, 15, 0.05)
self._visibility["nlp_augmentations_class"] = -1
@dataclass
class ConfigNLPCausalLMPrediction(DefaultConfig):
metric_class: Any = text_causal_language_modeling_metrics.Metrics
metric: str = "GPT"
metric_gpt_model: str = "gpt-3.5-turbo-0301"
metric_gpt_template: str = "general"
min_length_inference: int = 2
max_length_inference: int = 256
max_time: float = 0
batch_size_inference: int = 0
do_sample: bool = False
num_beams: int = 1
temperature: float = 0.0
repetition_penalty: float = 1.0
stop_tokens: str = ""
top_k: int = 0
top_p: float = 1.0
num_history: int = 4
def __post_init__(self):
super().__post_init__()
self._possible_values["metric"] = self.metric_class.names()
self._possible_values["metric_gpt_model"] = possible_values.String(
values=(
"gpt-3.5-turbo-0301",
"gpt-3.5-turbo-0613",
"gpt-4-0314",
"gpt-4-0613",
"gpt-4-1106-preview",
),
allow_custom=True,
)
self._possible_values["metric_gpt_template"] = possible_values.String(
values=(f.split(".")[0] for f in os.listdir("prompts"))
)
self._possible_values["batch_size_inference"] = (0, 512, 1)
self._possible_values["min_length_inference"] = (0, 1024, 1)
self._possible_values["max_length_inference"] = (1, 4096, 1)
self._possible_values["max_time"] = (0.0, 600.0, 1.0)
self._possible_values["num_beams"] = (1, 4, 1)
self._possible_values["temperature"] = (0, 10, 0.05)
self._possible_values["repetition_penalty"] = (1, 10, 0.05)
self._possible_values["top_k"] = (0, 100, 1)
self._possible_values["top_p"] = (0.5, 1, 0.05)
self._possible_values["num_history"] = (1, 50, 1)
self._visibility["metric_class"] = -1
# possible values for num_history are only used in chatbot tab
self._visibility["num_history"] = -1
self._nesting.add(
["metric_gpt_model", "metric_gpt_template"],
[Dependency(key="metric", value="GPT", is_set=True)],
)
@dataclass
class ConfigNLPCausalLMEnvironment(DefaultConfig):
gpus: Tuple[str, ...] = tuple(str(x) for x in range(torch.cuda.device_count()))
mixed_precision: bool = True
mixed_precision_dtype: str = "bfloat16"
compile_model: bool = False
use_deepspeed: bool = False
deepspeed_method: str = "ZeRO2"
deepspeed_allgather_bucket_size: int = int(1e6)
deepspeed_reduce_bucket_size: int = int(1e6)
deepspeed_stage3_prefetch_bucket_size: int = int(1e6)
deepspeed_stage3_param_persistence_threshold: int = int(1e6)
# deepspeed_offload_optimizer: bool = False
# deepspeed_stage3_max_live_parameters: int = 1e9
# deepspeed_stage3_max_reuse_distance: int = 1e9
find_unused_parameters: bool = False
trust_remote_code: bool = True
huggingface_branch: str = "main"
number_of_workers: int = 4
seed: int = -1
_seed: int = 0 # internal seed set in train.py (equals seed if seed is not -1)
_distributed: bool = False
_distributed_inference: bool = True
_local_rank: int = 0
_world_size: int = 1
_curr_step: int = 0
_curr_val_step: int = 0
_rank: int = 0 # global rank
_device: str = "cuda"
_cpu_comm: Any = None
_model_card_template: str = "text_causal_language_modeling_model_card_template.md"
_summary_card_template: str = (
"text_causal_language_modeling_experiment_summary_card_template.md"
)
def __post_init__(self):
super().__post_init__()
self._possible_values["gpus"] = possible_values.String(
values=tuple(
[(str(x), f"GPU #{x+1}") for x in range(torch.cuda.device_count())]
),
allow_custom=False,
)
self._possible_values["mixed_precision_dtype"] = possible_values.String(
values=("bfloat16", "float16"),
allow_custom=False,
)
self._possible_values["number_of_workers"] = (1, multiprocessing.cpu_count(), 1)
self._possible_values["seed"] = possible_values.Number(step=1, min=-1)
self._possible_values["deepspeed_method"] = ["ZeRO2", "ZeRO3"]
self._possible_values["deepspeed_allgather_bucket_size"] = (
possible_values.Number(step=1, min=1e6)
)
self._possible_values["deepspeed_reduce_bucket_size"] = possible_values.Number(
step=1, min=1e6
)
self._possible_values["deepspeed_stage3_prefetch_bucket_size"] = (
possible_values.Number(step=1, min=1e6)
)
self._possible_values["deepspeed_stage3_param_persistence_threshold"] = (
possible_values.Number(step=1, min=1e6)
)
self._possible_values["deepspeed_stage3_max_live_parameters"] = (
possible_values.Number(step=1, min=1e6)
)
self._possible_values["deepspeed_stage3_max_reuse_distance"] = (
possible_values.Number(step=1, min=1e6)
)
self._nesting.add(
[
"mixed_precision_dtype",
],
[Dependency(key="mixed_precision", value=True, is_set=True)],
)
self._nesting.add(
[
"deepspeed_method",
"deepspeed_reduce_bucket_size",
],
[Dependency(key="use_deepspeed", value=True, is_set=True)],
)
self._nesting.add(
[
"deepspeed_allgather_bucket_size",
],
[
Dependency(key="use_deepspeed", value=True, is_set=True),
Dependency(key="deepspeed_method", value="ZeRO2", is_set=True),
],
)
self._nesting.add(
[
"deepspeed_stage3_prefetch_bucket_size",
"deepspeed_stage3_param_persistence_threshold",
# "deepspeed_offload_optimizer",
],
[
Dependency(key="use_deepspeed", value=True, is_set=True),
Dependency(key="deepspeed_method", value="ZeRO3", is_set=True),
],
)
# self._nesting.add(
# [
# "deepspeed_stage3_max_live_parameters",
# "deepspeed_stage3_max_reuse_distance",
# ],
# [Dependency(key="deepspeed_offload_optimizer", value=False, is_set=False)], # noqa: E501
# )
@dataclass
class ConfigNLPCausalLMLogging(DefaultConfig):
logger: str = "None"
neptune_project: str = ""
_neptune_debug: bool = False
plots_class: Any = text_causal_language_modeling_plots.Plots
# the actual logger, will be set dynamically at runtime
_logger: Any = None
def __post_init__(self):
super().__post_init__()
self._possible_values["logger"] = Loggers.names()
self._nesting.add(
["neptune_project"],
[Dependency(key="logger", value="Neptune", is_set=True)],
)
self._visibility["plots_class"] = -1
@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: ConfigNLPCausalLMDataset = field(default_factory=ConfigNLPCausalLMDataset)
tokenizer: ConfigNLPCausalLMTokenizer = field(
default_factory=ConfigNLPCausalLMTokenizer
)
architecture: ConfigNLPCausalLMArchitecture = field(
default_factory=ConfigNLPCausalLMArchitecture
)
training: ConfigNLPCausalLMTraining = field(
default_factory=ConfigNLPCausalLMTraining
)
augmentation: ConfigNLPAugmentation = field(default_factory=ConfigNLPAugmentation)
prediction: ConfigNLPCausalLMPrediction = field(
default_factory=ConfigNLPCausalLMPrediction
)
environment: ConfigNLPCausalLMEnvironment = field(
default_factory=ConfigNLPCausalLMEnvironment
)
logging: ConfigNLPCausalLMLogging = field(default_factory=ConfigNLPCausalLMLogging)
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.prediction.temperature > 0 and not self.prediction.do_sample:
errors["title"] += ["Do sample needs to be enabled for temperature > 0"]
errors["message"] += [
"Please enable do sample if you want to use temperature > 0."
]
if self.prediction.temperature == 0 and self.prediction.do_sample:
errors["title"] += ["Temperature needs to be > 0 for do sample"]
errors["message"] += [
"Please increase temperature if you want to use do sample."
]
return errors
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