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| # coding=utf-8 | |
| # Copyright 2023 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. | |
| """ RWKV configuration""" | |
| from transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| logger = logging.get_logger(__name__) | |
| RWKV6_PRETRAINED_CONFIG_ARCHIVE_MAP = {} | |
| class Rwkv6Config(PretrainedConfig): | |
| """ | |
| This is the configuration class to store the configuration of a [`Rwkv6Model`]. It is used to instantiate a RWKV6 | |
| 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 RWVK-4 | |
| [RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) 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 65536): | |
| Vocabulary size of the RWKV6 model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`Rwkv6Model`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the embeddings and hidden states. | |
| num_hidden_layers (`int`, *optional*, defaults to 24): | |
| Number of hidden layers in the model. | |
| attention_hidden_size (`int`, *optional*): | |
| Dimensionality of the attention hidden states. Will default to `hidden_size` if unset. | |
| num_attention_heads (`int`, *optional*, defaults to 64): | |
| The attention heads to use in rwkv6 self_attention module. | |
| head_size (`int`, *optional*, defaults to 64): head_size of rwkv6 self_attention module. | |
| intermediate_size (`int`, *optional*): | |
| Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset. | |
| layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): | |
| The epsilon to use in the layer normalization layers. | |
| bos_token_id (`int`, *optional*, defaults to 0): | |
| The id of the beginning of sentence token in the vocabulary. Defaults to 0. | |
| eos_token_id (`int`, *optional*, defaults to 0): | |
| The id of the end of sentence token in the vocabulary. Defaults to 0. | |
| rescale_every (`int`, *optional*, defaults to 6): | |
| At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every | |
| `rescale_every` layer. If set to 0 or a negative number, no rescale is done. | |
| tie_word_embeddings (`bool`, *optional*, defaults to `False`): | |
| Whether or not to tie the word embeddings with the input token embeddings. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last state. | |
| Example: | |
| ```python | |
| >>> from transformers import Rwkv6Config, Rwkv6Model | |
| >>> # Initializing a Rwkv6 configuration | |
| >>> configuration = Rwkv6Config() | |
| >>> # Initializing a model (with random weights) from the configuration | |
| >>> model = Rwkv6Model(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "rwkv6" | |
| def __init__( | |
| self, | |
| vocab_size=65536, | |
| hidden_size=768, | |
| num_hidden_layers=24, | |
| attention_hidden_size=None, | |
| head_size=64, | |
| head_size_divisor=8, | |
| intermediate_size=None, | |
| layer_norm_epsilon=1e-5, | |
| bos_token_id=0, | |
| eos_token_id=0, | |
| rescale_every=6, | |
| tie_word_embeddings=False, | |
| use_cache=True, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size | |
| self.head_size = head_size | |
| self.head_size_divisor = head_size_divisor | |
| self.intermediate_size = None | |
| self.layer_norm_epsilon = layer_norm_epsilon | |
| self.rescale_every = rescale_every | |
| self.use_cache = use_cache | |
| self.bos_token_id = bos_token_id | |
| self.eos_token_id = eos_token_id | |
| super().__init__( | |
| tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs | |
| ) | |