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# coding=utf-8
# Copyright 2024 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
"""MixtureOfTokens configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class MoTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`MoTModel`]. It is used to
instantiate a MixtureOfTokens 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 MixtureOfTokens
[mot](https://huggingface.co/mot) architecture.
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 50257):
Vocabulary size of the MixtureOfTokens model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MoTModel`].
n_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
n_embd (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
n_expert (`int`, *optional*, defaults to 32):
The number of experts.
group_size (`int`, *optional*, defaults to 32):
The number of tokens per expert.
expert_size (`int`, *optional*):
The dimensionality of an expert. `None` will set it to n_inner / n_head.
init_scale (`float`, *optional*, defaults to 1.0):
The scaling factor for the initialization of MoTMLP weights. Inactive when creating through `from_pretrained`.
activation_function (`str`, *optional*, defaults to `"gelu_new"`):
Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
bos_token_id (`int`, *optional*, defaults to 50256):
Id of the beginning of sentence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 50256):
Id of the end of sentence token in the vocabulary.
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
emit_softmax_over_experts (`bool`, *optional*, defaults to `False`):
Determines the method of redistributing aggregated tokens in the MoT MLP. By default the model uses the merge weights.
This flag switches it to taking a softmax over the experts.
use_discrete_routing (`bool`, *optional*, defaults to `False`):
Discretize the mixing, sending only to the expert with the highest weight. Inference-only.
Example:
```python
>>> from transformers import MoTConfig, MoTModel
>>> # Initializing a MoT configuration
>>> configuration = MoTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = MoTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mot"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=50257,
n_positions=1024,
n_embd=768,
n_layer=12,
n_head=12,
n_inner=None,
n_expert=32,
group_size=32,
expert_size=None,
init_scale=1.0,
activation_function="gelu_new",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
emit_softmax_over_experts=False,
use_discrete_routing=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.n_expert = n_expert
self.group_size = group_size
self.expert_size = expert_size
self.init_scale = init_scale
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.emit_softmax_over_experts = emit_softmax_over_experts
self.use_discrete_routing = use_discrete_routing
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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