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# coding=utf-8
#
# 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.
from transformers import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class BaichuanM1Config(PretrainedConfig):
r"""
Configuration objects inherit from [`PretrainedConfig`] and control the behavior of model outputs. For more details,
refer to the documentation of [`PretrainedConfig`].
Args:
vocab_size (`int`, *optional*, defaults to 133120):
The size of the vocabulary used by the model.
hidden_size (`int`, *optional*, defaults to 4096):
The dimensionality of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
The dimensionality of the intermediate (MLP) representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
The number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
The number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
The number of key-value heads used to implement Grouped Query Attention (GQA).
- If `num_key_value_heads == num_attention_heads`, the model uses Multi-Head Attention (MHA).
- If `num_key_value_heads == 1`, the model uses Multi-Query Attention (MQA).
- Otherwise, the model uses Grouped Query Attention (GQA).
When converting a multi-head checkpoint to a GQA checkpoint, each group's key and value heads are constructed
by mean-pooling the original heads within that group. For more details, refer to [this paper](https://arxiv.org/pdf/2305.13245.pdf).
If not specified, this defaults to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (either a string or a callable function) used in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length the model can handle.
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-06):
The epsilon value used by the RMS normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/value attentions. This is only relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie the model's input and output word embeddings.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the Rotary Position Embeddings (RoPE).
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to enable sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
The size of the sliding window for sliding window attention (SWA). If not specified, it defaults to `2048`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio applied to the attention probabilities.
"""
model_type = "baichuan"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=133120,
hidden_size=5120,
intermediate_size=17408,
num_hidden_layers=40,
num_attention_heads=40,
num_key_value_heads=2,
num_swa_attention_heads: int = 20,
num_swa_key_value_heads=8,
sliding_window_layers: list = None,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_theta=100000.0,
sliding_window=2048,
attention_dropout=0.0,
conv_window = 2,
**kwargs,
):
self.sliding_window_layers = sliding_window_layers
self.num_swa_key_value_heads = num_swa_key_value_heads
self.num_swa_attention_heads = num_swa_attention_heads
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.sliding_window = sliding_window
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
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.attention_dropout = attention_dropout
self.conv_window = conv_window
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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