# 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)