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"""LongcatFlash model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
LONGCAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class LongcatFlashConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LongcatFlashModel`]. It is used to instantiate an LongcatFlash
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 LongcatFlash.
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 131072):
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LongcatFlashModel`]
hidden_size (`int`, *optional*, defaults to 7168):
Dimension of the hidden representations.
ffn_hidden_size (`int`, *optional*, defaults to 18432):
Dimension of the MLP representations.
expert_ffn_hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the MoE representations.
num_layers (`int`, *optional*, defaults to 61):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 128):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 128):
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 checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
n_routed_experts (`int`, *optional*, defaults to 256):
Number of routed experts.
routed_scaling_factor (`float`, *optional*, defaults to 2.5):
Scaling factor or routed experts.
kv_lora_rank (`int`, *optional*, defaults to 512):
Rank of the LoRA matrices for key and value projections.
q_lora_rank (`int`, *optional*, defaults to 1536):
Rank of the LoRA matrices for query projections.
qk_rope_head_dim (`int`, *optional*, defaults to 64):
Dimension of the query/key heads that use rotary position embeddings.
v_head_dim (`int`, *optional*, defaults to 128):
Dimension of the value heads.
qk_nope_head_dim (`int`, *optional*, defaults to 128):
Dimension of the query/key heads that don't use rotary position embeddings.
norm_topk_prob (`bool`, *optional*, defaults to `True`):
Whether to normalize the weights of the routed experts.
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 4096):
The maximum sequence length that this model might ever be used with.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
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.
attention_bias (`bool`, defaults to `False`, *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.
attention_method (`str`, *optional*, defaults to `"MLA"`):
The attention method to use.
initializer_range (`float`, *optional*, defaults to 0.006):
The initializer range for the model.
router_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the router.
zero_expert_num (`int`, *optional*, defaults to `None`):
The number of zero experts to use.
zero_expert_type (`str`, *optional*, defaults to `None`):
The type of zero expert to use.
```python
>>> from transformers import LongcatFlashModel, LongcatFlashConfig
>>> # Initializing a LongcatFlash style configuration
>>> configuration = LongcatFlashConfig()
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "longcat_flash"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.experts.*.gate_proj": "local_colwise",
"layers.*.mlp.experts.*.up_proj": "local_colwise",
"layers.*.mlp.experts.*.down_proj": "local_rowwise",
"layers.*.mlps.*.gate_proj": "local_colwise",
"layers.*.mlps.*.up_proj": "local_colwise",
"layers.*.mlps.*.down_proj": "local_rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=131072,
hidden_size=7168,
ffn_hidden_size=18432,
expert_ffn_hidden_size=2048,
num_layers=61,
num_attention_heads=128,
num_key_value_heads=None,
n_routed_experts=256,
routed_scaling_factor=1,
kv_lora_rank=512,
q_lora_rank=1536,
qk_rope_head_dim=64,
v_head_dim=128,
qk_nope_head_dim=128,
mla_scale_q_lora=True,
mla_scale_kv_lora=True,
moe_topk=8,
norm_topk_prob=False,
hidden_act="silu",
max_position_embeddings=4096,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=0,
eos_token_id=1,
tie_word_embeddings=False,
rope_theta=10000.0,
attention_bias=False,
attention_dropout=0.0,
attention_method='MLA',
initializer_range=0.006,
router_bias=False,
zero_expert_num=None,
zero_expert_type=None,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.ffn_hidden_size = ffn_hidden_size
self.expert_ffn_hidden_size = expert_ffn_hidden_size
self.num_layers = num_layers
self.num_attention_heads = num_attention_heads
self.n_routed_experts = n_routed_experts
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
self.moe_topk = moe_topk
self.norm_topk_prob = norm_topk_prob
self.mla_scale_q_lora = mla_scale_q_lora
self.mla_scale_kv_lora = mla_scale_kv_lora
self.attention_method = attention_method
self.initializer_range = initializer_range
self.router_bias = router_bias
self.zero_expert_num = zero_expert_num
self.zero_expert_type = zero_expert_type
if self.attention_method == "MLA":
self.head_dim = qk_rope_head_dim
else:
ValueError('attention_method should be one of ["MLA"]')
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.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
rope_config_validation(self)
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,
)
@property
def num_hidden_layers(self):
return self.num_layers
__all__ = ["LongcatFlashConfig"]
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