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from dataclasses import dataclass, asdict
import yaml
from pprint import pprint
from .utils import is_main
import os
from pathlib import Path
import uuid
def load_config(path, config_dir=Path("configs")):
if not path.endswith(".yml"):
path += ".yml"
if not os.path.exists(path):
path = config_dir / path
with open(path, "r") as stream:
config = yaml.safe_load(stream)
return config
@dataclass
class MultimodalConfig:
# Training:
# ------------------------------------------------------------
batch_size: int
train_steps: int
optimizer_name: str = "AdamW"
lr: float = 8.0e-4
image_enc_lr: float = None
min_lr: float = 0.0
lr_decay_iters: int = None
gradient_accumulation_steps: int = 1
image_size: int = 256
eval_every: int = 250
eval_steps: int = 25
zero_stage: int = 2
gradient_clipping: float = 1.0
warmup_num_steps: int = 100
weight_decay: float = 0.00
run_blind: bool = False
fine_tune: bool = False
load_optimizer: bool = True
# Checkpointing:
# ------------------------------------------------------------
save_every: int = 2500
save: str = None
load: str = None
# Data:
# ------------------------------------------------------------
train_dataset_name: str = "conceptual_captions"
eval_dataset_name: str = "/data/conceptual_captions"
train_dataset_dir: str = "/data/coco_data"
eval_dataset_dir: str = "/data/coco_data"
eval_dataset_pct: float = 0.1
# Model architecture:
# ------------------------------------------------------------
encoder_name: str = "clip"
tokenizer_name: str = "gpt2"
lm_name: str = "EleutherAI/gpt-j-6B"
image_seq_len: int = 2
pretrained_img_encoder: bool = False
seq_len: int = None
# Layer Freezing settings:
# ------------------------------------------------------------
freeze_lm: bool = True
freeze_img_encoder: bool = True
image_embed_dropout_prob: float = 0.0
use_image_embed_layernorm: bool = False
# Adapter settings:
# ------------------------------------------------------------
adapter_config: dict = None
# Classification Finetuning settings:
# ------------------------------------------------------------
class_dict: dict = None # {num_classes: .., ckpt_path: .., classifier_type:, .., interface_type: .., interface_position: .., freeze_model: ..}
# Logging settings:
# ------------------------------------------------------------
name: str = None # name, just used for wandb logging
log_every: int = 1
wandb_project: str = "magma"
def print(self):
if is_main():
print("-" * 100)
pprint(self.__dict__, indent=4)
print("-" * 100)
def __post_init__(self):
self.is_classifier = self.class_dict is not None
if self.adapter_config is None:
self.adapter_config = {}
# Deepspeed Settings:
# ------------------------------------------------------------
if self.lr_decay_iters is None:
self.lr_scheduler = "WarmupLR"
self.scheduler_dict = {
"type": self.lr_scheduler,
"params": {
"warmup_min_lr": self.min_lr,
"warmup_max_lr": self.lr,
"warmup_num_steps": self.warmup_num_steps,
},
}
else:
self.lr_scheduler = "WarmupDecayLR"
self.scheduler_dict = {
"type": self.lr_scheduler,
"params": {
"total_num_steps": self.lr_decay_iters,
"warmup_min_lr": self.min_lr,
"warmup_max_lr": self.lr,
"warmup_num_steps": self.warmup_num_steps,
},
}
self.deepspeed_config_params = {
"train_batch_size": self.batch_size,
"gradient_accumulation_steps": self.gradient_accumulation_steps,
"gradient_clipping": self.gradient_clipping,
"fp16": {"enabled": True, "loss_scale_window": 250},
"scheduler": self.scheduler_dict,
"zero_optimization": {
"stage": self.zero_stage,
"load_from_fp32_weights": False,
},
}
if self.name is None:
self.name = str(uuid.uuid4())[:8]
@classmethod
def from_yml(cls, path):
return cls(**load_config(path))
def to_dict(self):
return asdict(self)
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