|
""" |
|
OLMo configuration |
|
""" |
|
|
|
from transformers import AutoConfig, PretrainedConfig |
|
from transformers.utils import logging |
|
|
|
from .config import ModelConfig |
|
from .aliases import PathOrStr |
|
from .beam_search import Sampler |
|
from .exceptions import OLMoError |
|
from .initialization import ModuleType |
|
from .optim import Optimizer |
|
from .util import StrEnum |
|
from .safetensors_util import STKey |
|
from .torch_util import seed_all |
|
|
|
logger = logging.get_logger(__name__) |
|
|
|
|
|
class OLMoConfig(PretrainedConfig): |
|
model_type = "olmo" |
|
keys_to_ignore_at_inference = ["past_key_values"] |
|
|
|
def __init__(self, use_cache: bool = False, **kwargs): |
|
model_config = ModelConfig() |
|
all_kwargs = model_config.asdict() |
|
all_kwargs.update(kwargs) |
|
all_kwargs.update({"use_cache": use_cache}) |
|
all_kwargs.update( |
|
{ |
|
"architectures": all_kwargs.get("architectures", ["OLMoModelForCausalLM"]) |
|
or ["OLMoModelForCausalLM"] |
|
} |
|
) |
|
super().__init__(**all_kwargs) |
|
|
|
@property |
|
def num_attention_heads(self): |
|
return self.n_heads |
|
|
|
@property |
|
def num_hidden_layers(self): |
|
return self.n_layers |
|
|
|
@property |
|
def hidden_size(self): |
|
return self.d_model |
|
|
|
|
|
|
|
|
|
|
