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from typing import Literal
from transformers import PretrainedConfig
class EmuConfig(PretrainedConfig):
_auto_class = "AutoConfig"
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
hidden_act='silu',
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-06,
model_version: Literal["base", "chat"] = "base",
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
use_cache=True,
pretraining_tp=1,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
self.rms_norm_eps = rms_norm_eps
self.initializer_range = initializer_range
self.vocab_size = vocab_size
self.num_hidden_layers = num_hidden_layers
self.hidden_act = hidden_act
self.model_version = model_version
self.use_cache = use_cache
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self._rope_scaling_validation()
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
def _rope_scaling_validation(self):
"""
Validate the `rope_scaling` configuration.
"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scaling_factor = self.rope_scaling.get("factor", None)
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
)
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") |