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"""IQuestCoder model configuration."""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class IQuestCoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`IQuestCoderModel`]. It is used to instantiate
an IQuestCoder model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 76800):
Vocabulary size of the IQuestCoder model. Defines the number of different tokens that can be represented
by the `inputs_ids` passed when calling [`IQuestCoderModel`].
hidden_size (`int`, *optional*, defaults to 5120):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 27648):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 40):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention (GQA).
If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA).
If `num_key_value_heads=1`, the model will use Multi Query Attention (MQA).
head_dim (`int`, *optional*, defaults to 128):
The dimension of each attention head. If not specified, defaults to `hidden_size // num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 16384):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms 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).
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings.
rope_theta (`float`, *optional*, defaults to 500000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Supports various RoPE scaling
types including "linear", "dynamic", "yarn", "longrope", etc.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
clip_qkv (`float`, *optional*):
If set, clip the query, key, and value tensors to this value. Borrowed from OLMo for training stability.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention. Borrowed from Qwen2.
sliding_window (`int`, *optional*):
The sliding window size. Only effective when `use_sliding_window=True`.
max_window_layers (`int`, *optional*, defaults to 0):
The number of layers that don't use sliding window attention. Borrowed from Qwen2.
Example:
```python
>>> from configuration_iquestcoder import IQuestCoderConfig
>>> from modeling_iquestcoder import IQuestCoderModel
>>> # Initializing a IQuestCoder configuration
>>> configuration = IQuestCoderConfig()
>>> # Initializing a model from the configuration
>>> model = IQuestCoderModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "iquestcoder"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=76800,
hidden_size=5120,
intermediate_size=27648,
num_hidden_layers=80,
num_attention_heads=40,
num_key_value_heads=8,
head_dim=128,
hidden_act="silu",
max_position_embeddings=16384,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=1,
eos_token_id=2,
tie_word_embeddings=False,
rope_theta=500000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
# IQuestCoder specific (borrowed from OLMo)
clip_qkv=None,
# IQuestCoder specific (borrowed from Qwen2)
use_sliding_window=False,
sliding_window=None,
max_window_layers=0,
**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.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
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.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
# IQuestCoder specific
self.clip_qkv = clip_qkv
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window
self.max_window_layers = max_window_layers
# Validate rope_scaling configuration
self._rope_scaling_validation()
super().__init__(
pad_token_id=pad_token_id,
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) < 1:
raise ValueError(
"`rope_scaling` must be a dictionary with a minimum of one field, `type` or `rope_type`."
)
rope_scaling_type = self.rope_scaling.get("type", None) or self.rope_scaling.get("rope_type", None)
if rope_scaling_type is None:
raise ValueError(
"`rope_scaling` must have a `type` or `rope_type` field."
)
valid_rope_types = ["linear", "dynamic", "yarn", "longrope", "llama3"]
if rope_scaling_type not in valid_rope_types:
raise ValueError(
f"`rope_scaling`'s type field must be one of {valid_rope_types}, got {rope_scaling_type}"
)
__all__ = ["IQuestCoderConfig"]
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