|
|
|
|
|
|
|
|
|
|
|
from transformers import PretrainedConfig |
|
|
|
|
|
class QWenConfig(PretrainedConfig): |
|
model_type = "qwen" |
|
keys_to_ignore_at_inference = ["past_key_values"] |
|
|
|
def __init__( |
|
self, |
|
vocab_size=151936, |
|
hidden_size=4096, |
|
num_hidden_layers=32, |
|
num_attention_heads=32, |
|
emb_dropout_prob=0.0, |
|
attn_dropout_prob=0.0, |
|
layer_norm_epsilon=1e-6, |
|
initializer_range=0.02, |
|
max_position_embeddings=8192, |
|
scale_attn_weights=True, |
|
use_cache=True, |
|
bf16=False, |
|
fp16=False, |
|
fp32=False, |
|
kv_channels=128, |
|
rotary_pct=1.0, |
|
rotary_emb_base=10000, |
|
use_dynamic_ntk=True, |
|
use_logn_attn=True, |
|
use_flash_attn="auto", |
|
intermediate_size=22016, |
|
no_bias=True, |
|
tie_word_embeddings=False, |
|
use_cache_quantization=False, |
|
use_cache_kernel=False, |
|
softmax_in_fp32=False, |
|
**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 |
|
self.emb_dropout_prob = emb_dropout_prob |
|
self.attn_dropout_prob = attn_dropout_prob |
|
self.layer_norm_epsilon = layer_norm_epsilon |
|
self.initializer_range = initializer_range |
|
self.scale_attn_weights = scale_attn_weights |
|
self.use_cache = use_cache |
|
self.max_position_embeddings = max_position_embeddings |
|
self.bf16 = bf16 |
|
self.fp16 = fp16 |
|
self.fp32 = fp32 |
|
self.kv_channels = kv_channels |
|
self.rotary_pct = rotary_pct |
|
self.rotary_emb_base = rotary_emb_base |
|
self.use_dynamic_ntk = use_dynamic_ntk |
|
self.use_logn_attn = use_logn_attn |
|
self.use_flash_attn = use_flash_attn |
|
self.no_bias = no_bias |
|
self.use_cache_quantization = use_cache_quantization |
|
self.use_cache_kernel = use_cache_kernel |
|
self.softmax_in_fp32 = softmax_in_fp32 |
|
super().__init__( |
|
tie_word_embeddings=tie_word_embeddings, |
|
**kwargs |
|
) |
|
|
