# Copyright 2025 The EasyDeL Author @erfanzar (Erfan Zare Chavoshi).
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from eformer.common_types import ColumnWise, Replicated, RowWise
from easydel.infra.base_module import EasyDeLBaseConfig
from easydel.infra.factory import register_config
[docs]@register_config("glm4")
class Glm4Config(EasyDeLBaseConfig):
r"""
This is the configuration class to store the configuration of a [`Glm4Model`]. It is used to instantiate an Glm4
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Glm4-4-9b-chat.
e.g. [THUDM/GLM-4-9B-0414](https://huggingface.co/THUDM/GLM-4-9B-0414)
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 151552):
Vocabulary size of the Glm4 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Glm4Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 13696):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 40):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 2):
This is the number of key_value heads that should be used to implement Grouped Query Attention. 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) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
`num_attention_heads`.
partial_rotary_factor (`float`, *optional*, defaults to 0.5): The factor of the partial rotary position.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The legacy activation function. It is overwritten by the `hidden_activation`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 131072):
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 1.5625e-07):
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). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
pad_token_id (`int`, *optional*, defaults to 151329):
Padding token id.
eos_token_id (`int` | `list`, *optional*, defaults to `[151329, 151336, 151338]`):
End of stream token id.
bos_token_id (`int`, *optional*):
Beginning of stream token id.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `True`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
```python
>>> from transformers import Glm4Model, Glm4Config
>>> # Initializing a Glm4 glm4-4-9b-chat style configuration
>>> configuration = Glm4Config()
>>> # Initializing a model from the glm4-4-9b-chat style configuration
>>> model = Glm4Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "glm4"
def __init__(
self,
vocab_size=151552,
hidden_size=4096,
intermediate_size=13696,
num_hidden_layers=40,
num_attention_heads=32,
num_key_value_heads=2,
partial_rotary_factor=0.5,
head_dim=128,
hidden_act="silu",
attention_dropout=0.0,
max_position_embeddings=131072,
initializer_range=0.02,
rms_norm_eps=0.00000015625,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000.0,
pad_token_id=151329,
eos_token_id=None,
bos_token_id=None,
attention_bias=True,
**kwargs,
):
if eos_token_id is None:
eos_token_id = [151329, 151336, 151338]
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.partial_rotary_factor = partial_rotary_factor
self.head_dim = head_dim
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_bias = attention_bias
self.attention_dropout = attention_dropout
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,
)
[docs] def get_partition_rules(self, *args, **kwargs):
"""
Get the partition rules for the Llama model.
Returns:
`tp.Tuple[tp.Tuple[str, PartitionSpec]]`: The partition rules.
"""
pmag = self.partition_manager
return (
# Column-wise Sharding (split output dimensions)
(r".*attn/.*(q_proj|k_proj|v_proj)/kernel", pmag.resolve(ColumnWise)),
# QKV Projections
(r".*mlp/(gate_proj|up_proj)/kernel", pmag.resolve(ColumnWise)),
# MLP Up-Projections
(r".*embed_tokens/embedding", pmag.resolve(ColumnWise)), # Token Embeddings
(r".*embed_vision/embedding", pmag.resolve(ColumnWise)), # Vision Embeddings
(r".*lm_head/kernel", pmag.resolve(ColumnWise)), # Language Model Head
(r".*vision_head/kernel", pmag.resolve(ColumnWise)), # Vision Model Head
# Row-wise Sharding (split input dimensions)
(r".*attn/o_proj/kernel", pmag.resolve(RowWise)), # Attention Output
(r".*mlp/down_proj/kernel", pmag.resolve(RowWise)), # MLP Down-Projection
(r".*score/kernel", pmag.resolve(RowWise)), # Sequence Classifier Head
# Replicated Parameters
(r".*bias", pmag.resolve(Replicated)), # All biases
(r".*layernorm/scale", pmag.resolve(Replicated)), # LayerNorm scales
(r".*rms_norm/scale", pmag.resolve(Replicated)), # RMSNorm scales
(r".*norm/scale", pmag.resolve(Replicated)), # Final LayerNorm scale
(r".*", pmag.resolve(Replicated)),
)
__all__ = ["Glm4Config"]