You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
157 lines
7.4 KiB
157 lines
7.4 KiB
# Copyright 2024 JetMoe AI and the HuggingFace Inc. team. 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.
|
|
"""JetMoe model configuration"""
|
|
|
|
from ...configuration_utils import PreTrainedConfig
|
|
from ...modeling_rope_utils import RopeParameters
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class JetMoeConfig(PreTrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`JetMoeModel`]. It is used to instantiate a
|
|
JetMoe model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
|
with the defaults will yield a configuration of the JetMoe-4B.
|
|
|
|
[jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)
|
|
|
|
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 32000):
|
|
Vocabulary size of the JetMoe model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`JetMoeModel`]
|
|
hidden_size (`int`, *optional*, defaults to 2048):
|
|
Dimension of the hidden representations.
|
|
num_hidden_layers (`int`, *optional*, defaults to 12):
|
|
Number of hidden layers in the Transformer encoder.
|
|
num_key_value_heads (`int`, *optional*, defaults to 16):
|
|
Number of attention heads for each key and value in the Transformer encoder.
|
|
kv_channels (`int`, *optional*, defaults to 128):
|
|
Defines the number of channels for the key and value tensors.
|
|
intermediate_size (`int`, *optional*, defaults to 5632):
|
|
Dimension of the MLP representations.
|
|
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
|
The maximum sequence length that this model might ever be used with. JetMoe's attention allows sequence of
|
|
up to 4096 tokens.
|
|
activation_function (`string`, *optional*, defaults to `"silu"`):
|
|
Defines the activation function for MLP experts.
|
|
num_local_experts (`int`, *optional*, defaults to 8):
|
|
Defines the number of experts in the MoE and MoA.
|
|
num_experts_per_tok (`int, *optional*, defaults to 2):
|
|
The number of experts to route per-token and for MoE and MoA.
|
|
output_router_logits (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the router logits should be returned by the model. Enabling this will also
|
|
allow the model to output the auxiliary loss.
|
|
aux_loss_coef (`float`, *optional*, defaults to 0.01):
|
|
The coefficient for the auxiliary loss.
|
|
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`.
|
|
bos_token_id (`int`, *optional*, defaults to 1):
|
|
The id of the "beginning-of-sequence" token.
|
|
eos_token_id (`int`, *optional*, defaults to 2):
|
|
The id of the "end-of-sequence" token.
|
|
pad_token_id (`int`, *optional*):
|
|
The id of the padding token.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
|
|
Whether the model's input and output word embeddings should be tied.
|
|
rope_parameters (`RopeParameters`, *optional*):
|
|
Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
|
|
a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
|
|
with longer `max_position_embeddings`.
|
|
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
|
|
The epsilon used by the rms normalization layers.
|
|
initializer_range (`float`, *optional*, defaults to 0.01):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
|
attention_dropout (`float`, *optional*, defaults to 0.0):
|
|
The dropout ratio for the attention probabilities.
|
|
|
|
```python
|
|
>>> from transformers import JetMoeModel, JetMoeConfig
|
|
|
|
>>> # Initializing a JetMoe 4B style configuration
|
|
>>> configuration = JetMoeConfig()
|
|
|
|
>>> # Initializing a model from the JetMoe 4B style configuration
|
|
>>> model = JetMoeModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "jetmoe"
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
|
attribute_map = {"head_dim": "kv_channels"}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size: int | None = 32000,
|
|
hidden_size: int | None = 2048,
|
|
num_hidden_layers: int | None = 12,
|
|
num_key_value_heads: int | None = 16,
|
|
kv_channels: int | None = 128,
|
|
intermediate_size: int | None = 5632,
|
|
max_position_embeddings: int | None = 4096,
|
|
activation_function: str | None = "silu",
|
|
num_local_experts: int | None = 8,
|
|
num_experts_per_tok: int | None = 2,
|
|
output_router_logits: bool | None = False,
|
|
aux_loss_coef: float | None = 0.01,
|
|
use_cache: bool | None = True,
|
|
bos_token_id: int | None = 1,
|
|
eos_token_id: int | None = 2,
|
|
pad_token_id: int | None = None,
|
|
tie_word_embeddings: bool | None = True,
|
|
rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
|
|
rms_norm_eps: int | None = 1e-6,
|
|
initializer_range: float | None = 0.01,
|
|
attention_dropout: float | None = 0.0,
|
|
**kwargs,
|
|
):
|
|
if num_experts_per_tok > num_local_experts:
|
|
raise ValueError("`num_experts_per_tok` must be less than or equal to `num_local_experts`")
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.num_attention_heads = num_key_value_heads * num_experts_per_tok
|
|
self.num_key_value_heads = num_key_value_heads
|
|
self.kv_channels = kv_channels
|
|
self.intermediate_size = intermediate_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.activation_function = activation_function
|
|
self.num_local_experts = num_local_experts
|
|
self.num_experts_per_tok = num_experts_per_tok
|
|
self.output_router_logits = output_router_logits
|
|
self.aux_loss_coef = aux_loss_coef
|
|
self.use_cache = use_cache
|
|
self.initializer_range = initializer_range
|
|
self.attention_dropout = attention_dropout
|
|
|
|
self.bos_token_id = bos_token_id
|
|
self.eos_token_id = eos_token_id
|
|
self.pad_token_id = pad_token_id
|
|
self.rms_norm_eps = rms_norm_eps
|
|
self.rope_parameters = rope_parameters
|
|
self.tie_word_embeddings = tie_word_embeddings
|
|
super().__init__(**kwargs)
|
|
|
|
|
|
__all__ = ["JetMoeConfig"]
|