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from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from transformers import PreTrainedTokenizer, TensorType, is_torch_available
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class NanoConfig(PretrainedConfig):
model_type = "nano"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "hidden_size",
"max_position_embeddings": "max_position_embeddings",
"num_attention_heads": "num_attention_heads",
"num_hidden_layers": "num_hidden_layers",
}
def __init__(
self,
vocab_size=32000,
max_position_embeddings=2048,
expanded_wte_size=None,
expanded_lm_head_size=None,
hidden_size=768,
kv_hidden_size=None, # in case you want to use cross-attention
num_hidden_layers=10,
num_attention_heads=12,
intermediate_size=None,
activation_function="silu",
layer_norm_epsilon=1e-6,
initializer_range=0.02,
use_cache=True,
bos_token_id=1,
eos_token_id=2,
combined_qkv=True,
use_bias=False,
lm_head_projection_bias=False,
lm_head_bias=False,
layernorm="llamarmsnorm", # layernorm, llamarmsnorm
rope_scaling=None,
rope_theta=10000,
ffn="llama-like",
experimental_full_adaption_rank = None, # 8
full_adaptation_has_pre_proj = True,
pre_proj_dim = 1536,
full_adaptation_type="no", # "lora", "no", "linear", "linear-r", "linear-ra"
tie_word_embeddings=False,
**kwargs,
):
self.pre_proj_dim = pre_proj_dim
self.full_adaptation_has_pre_proj = full_adaptation_has_pre_proj
self.full_adaptation_type = full_adaptation_type
self.tie_word_embeddings = tie_word_embeddings
self.experimental_full_adaption_rank = experimental_full_adaption_rank
self.ffn = ffn
self.rope_theta=rope_theta
self.layernorm = layernorm
self.rope_scaling=rope_scaling
self.lm_head_projection_bias = lm_head_projection_bias
self.kv_hidden_size = kv_hidden_size
self.lm_head_bias = lm_head_bias
self.use_bias = use_bias
self.expanded_wte_size = expanded_wte_size
self.expanded_lm_head_size = expanded_lm_head_size
self.combined_qkv = combined_qkv
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = (
intermediate_size if intermediate_size is not None else hidden_size * 4
)
self.activation_function = activation_function
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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