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# Modify the original configuration_llama.py to
# be compatiable with our training framework.
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class CustomLlamaConfig(PretrainedConfig):
"""
Args:
vocab_size (`int`, *optional*, defaults to 50432):
Vocabulary size of the WeLMV3 model. Defines the number of
different tokens that can be represented by the
`inputs_ids` passed when calling [`WeLMV3Model`].
hidden_size (`int`, *optional*, defaults to 6144):
Dimension of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 44):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the
Transformer encoder.
num_kv_heads (`int`, *optional*, defaults to 4):
Number of GQA groups.
intermediate_size (`int`, *optional*, defaults to 24576):
Dimension of the "intermediate" (i.e., feed-forward) layer in the
Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the
encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
rotary_pct (`float`, *optional*, defaults to 0.25):
percentage of hidden dimensions to allocate to rotary embeddings
rotary_emb_base (`int`, *optional*, defaults to 10000)
base for computing rotary embeddings frequency
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used
with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 1e-5):
The standard deviation of the truncated_normal_initializer for
initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values
attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
"""
model_type = "custom_llama"
def __init__(
self,
vocab_size=102400,
hidden_size=2560,
num_layers=32,
num_attention_heads=20,
num_kv_heads=4,
ffn_hidden_size=2560 * 4,
hidden_act="swiglu",
rotary_pct=1.0,
rotary_emb_base=10000,
rotary_compress=1.0,
max_position_embeddings=4096,
initializer_range=0.02,
layernorm_epsilon=1e-5,
use_cache=True,
bos_token_id=0,
eos_token_id=2,
rms_norm=None,
norm_type='layer_norm',
qkv_proj_bias=True,
out_proj_bias=True,
mlp_fc1_bias=True,
mlp_fc2_bias=True,
**kwargs,
):
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.num_kv_heads = num_kv_heads
self.ffn_hidden_size = ffn_hidden_size
self.hidden_act = hidden_act
self.rotary_pct = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.rotary_compress = rotary_compress
self.initializer_range = initializer_range
self.layernorm_epsilon = layernorm_epsilon
self.use_cache = use_cache
if rms_norm is not None:
self.norm_type = 'rms_norm' if rms_norm else 'layer_norm'
else:
self.norm_type = norm_type
self.qkv_proj_bias = qkv_proj_bias
self.out_proj_bias = out_proj_bias
self.mlp_fc1_bias = mlp_fc1_bias
self.mlp_fc2_bias = mlp_fc2_bias
self.num_hidden_layers = num_layers
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