|
from transformers import PretrainedConfig
|
|
|
|
|
|
class PhiConfig(PretrainedConfig):
|
|
model_type = "phi"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=51200,
|
|
hidden_size=2048,
|
|
intermediate_size=8192,
|
|
num_hidden_layers=24,
|
|
num_attention_heads=32,
|
|
num_key_value_heads=None,
|
|
resid_pdrop=0.0,
|
|
embd_pdrop=0.0,
|
|
attention_dropout=0.0,
|
|
hidden_act="gelu_new",
|
|
max_position_embeddings=2048,
|
|
initializer_range=0.02,
|
|
layer_norm_eps=1e-5,
|
|
use_cache=True,
|
|
tie_word_embeddings=False,
|
|
rope_theta=10000.0,
|
|
rope_scaling=None,
|
|
partial_rotary_factor=0.5,
|
|
qk_layernorm=False,
|
|
bos_token_id=1,
|
|
eos_token_id=2,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.intermediate_size = intermediate_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
|
|
if num_key_value_heads is None:
|
|
num_key_value_heads = num_attention_heads
|
|
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.resid_pdrop = resid_pdrop
|
|
self.embd_pdrop = embd_pdrop
|
|
self.attention_dropout = attention_dropout
|
|
self.hidden_act = hidden_act
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.initializer_range = initializer_range
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.use_cache = use_cache
|
|
self.rope_theta = rope_theta
|
|
self.rope_scaling = rope_scaling
|
|
self.partial_rotary_factor = partial_rotary_factor
|
|
self.qk_layernorm = qk_layernorm
|
|
self._rope_scaling_validation()
|
|
|
|
super().__init__(
|
|
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}"
|
|
)
|
|
|
|
|
|
class MoondreamConfig(PretrainedConfig):
|
|
model_type = "moondream1"
|
|
|
|
def __init__(self, **kwargs):
|
|
self.text_config = PhiConfig(**kwargs.pop("text_config", {}))
|
|
super().__init__(**kwargs)
|
|
|
