83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# Copyright (c) 2024, OrionStar Inc. All rights reserved.
|
|
|
|
from transformers import PretrainedConfig
|
|
|
|
class OrionConfig(PretrainedConfig):
|
|
model_type = "orion"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=84608,
|
|
hidden_size=4096,
|
|
intermediate_size=15360,
|
|
num_hidden_layers=40,
|
|
num_attention_heads=40,
|
|
num_key_value_heads=40,
|
|
hidden_act="silu",
|
|
max_position_embeddings=4096,
|
|
initializer_range=0.02,
|
|
rms_norm_eps=1e-5,
|
|
use_cache=True,
|
|
pad_token_id=None,
|
|
bos_token_id=1,
|
|
eos_token_id=2,
|
|
pretraining_tp=1,
|
|
tie_word_embeddings=False,
|
|
rope_theta=10000.0,
|
|
rope_scaling=None,
|
|
attention_bias=False,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
# for backward compatibility
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.hidden_act = hidden_act
|
|
self.initializer_range = initializer_range
|
|
self.rms_norm_eps = rms_norm_eps
|
|
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
|
|
|
|
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 an float > 1, got {rope_scaling_factor}")
|
|
|