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from transformers import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class CodeFuseCGELargeConfig(PretrainedConfig):
model_type = "qwen2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151936,
hidden_size=4096,
intermediate_size=22016,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=32,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
attention_dropout=0.0,
embedding_method="pma",
inf_seq_length=1024,
padding_side="right",
compress_dim=1024,
keep_max_layer=32,
pma_num_heads=32,
pma_ln=True,
pma_norm=False,
pma_norm_mode="post_normal",
**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
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if use_sliding_window else None
self.max_window_layers = max_window_layers
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.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.embedding_method = embedding_method
self.inf_seq_length = inf_seq_length
self.padding_side = padding_side
self.compress_dim = compress_dim
self.keep_max_layer = keep_max_layer
self.pma_num_heads = pma_num_heads
self.pma_ln = pma_ln
self.pma_norm = pma_norm
self.pma_norm_mode = pma_norm_mode
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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