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# Copyright 2025 Maincode. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Maincoder model configuration."""
from typing import Optional
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MaincoderConfig(PretrainedConfig):
r"""
Configuration class for Maincoder model.
Args:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Maincoder model.
hidden_size (`int`, *optional*, defaults to 1536):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimension of the MLP intermediate representations.
intermediate_size_mlp (`int`, *optional*, defaults to 4096):
Dimension of the MLP representations (same as intermediate_size for dense models).
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer.
num_key_value_heads (`int`, *optional*, defaults to 4):
Number of key-value heads for Grouped Query Attention (GQA).
head_dim (`int`, *optional*, defaults to 96):
Dimension of each attention head.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The activation function in the MLP.
max_position_embeddings (`int`, *optional*, defaults to 2048):
Maximum sequence length the model can handle.
initializer_range (`float`, *optional*, defaults to 0.02):
Standard deviation for weight initialization.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
Epsilon for RMS normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether to use key-value cache for generation.
pad_token_id (`int`, *optional*, defaults to 151643):
Padding token id.
bos_token_id (`int`, *optional*):
Beginning of sequence token id.
eos_token_id (`int`, *optional*, defaults to 151643):
End of sequence token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie input and output embeddings.
rope_theta (`float`, *optional*, defaults to 1000000.0):
Base period for RoPE embeddings.
rope_scaling (`Dict`, *optional*):
RoPE scaling configuration for extended context.
attention_dropout (`float`, *optional*, defaults to 0.0):
Dropout probability for attention weights.
use_qk_norm (`bool`, *optional*, defaults to `True`):
Whether to apply RMS normalization to query and key.
Example:
```python
>>> from configuration_maincoder import MaincoderConfig
>>> from modelling_maincoder import MaincoderForCausalLM
>>> config = MaincoderConfig()
>>> model = MaincoderForCausalLM(config)
```
"""
model_type = "maincoder"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size: int = 151936,
hidden_size: int = 1536,
intermediate_size: int = 4096,
intermediate_size_mlp: int = 4096,
num_hidden_layers: int = 32,
num_attention_heads: int = 16,
num_key_value_heads: Optional[int] = 4,
head_dim: Optional[int] = 96,
hidden_act: str = "silu",
max_position_embeddings: int = 2048,
initializer_range: float = 0.02,
rms_norm_eps: float = 1e-5,
use_cache: bool = True,
pad_token_id: Optional[int] = 151643,
bos_token_id: Optional[int] = None,
eos_token_id: int = 151643,
tie_word_embeddings: bool = True,
rope_theta: float = 1000000.0,
rope_scaling: Optional[dict] = None,
attention_dropout: float = 0.0,
use_qk_norm: bool = True,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.intermediate_size_mlp = intermediate_size_mlp
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.max_position_embeddings = max_position_embeddings
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_dropout = attention_dropout
self.use_qk_norm = use_qk_norm
self.hidden_act = hidden_act
# GQA configuration
self.num_key_value_heads = num_key_value_heads if num_key_value_heads is not None else num_attention_heads
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
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,
)
__all__ = ["MaincoderConfig"]
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