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""" RWKV configuration"""
from transformers.configuration_utils import PretrainedConfig
# from transformers.utils import logging
# logger = logging.get_logger(__name__)
# Import the dependencies
from .modeling_blocks_rwkv7 import RWKV7GooseConfigMap
class RWKV7Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Rwkv7Model`]. It is used to instantiate a RWKV7
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-7
[RWKV/v7-Goose-1.6B-Pile-HF](https://huggingface.co/RWKV/v7-Goose-1.6B-Pile-HF) 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 RWKV7 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Rwkv7Model`].
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the model.
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the embeddings and hidden states.
hidden_size_att (`int`, *optional*):
Dimensionality of the attention hidden states. Will be computed from `hidden_size` if unset.
hidden_size_ffn (`int`, *optional*):
Dimensionality of the FFN hidden states. Will be computed from `hidden_size` if unset.
head_size (`int`, *optional*, defaults to 64):
head_size of rwkv7 self_attention module.
tmix_backend (`str`, *optional*, defaults to "auto"):
Backend to use for the time mix module. "auto" defaults to "pytorch" if the device is "cpu" and "cuda" otherwise.
(Valid values: "auto", "pytorch", "cuda", "triton", "triton_bighead", "fla", "fla_fused", "pytorch_ref", "pytorch_ref_fp32")
init_state_wkv (`bool`, *optional*, defaults to `False`):
Whether to initialize the wkv state in the model. Used for WKV state tuning.
device (`str`, *optional*):
Device to use for the model. Use the respective torch.device types
dtype (`str`, *optional*):
Model weights data type. Use the respective torch.dtype types
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.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to tie the word embeddings with the input token embeddings.
(this value is currently ignored in our implementation)
Example:
```python
>>> from transformers import Rwkv7Config, Rwkv7Model
>>> # Initializing a Rwkv7 configuration
>>> configuration = Rwkv7Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = Rwkv7Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "rwkv7"
def __init__(
self,
########################################
# Vocab, layer count, and hidden size
vocab_size=65536,
num_hidden_layers=24,
hidden_size=768,
# Optional hidden sizes
hidden_size_att=None,
hidden_size_ffn=None,
# Headsize, timemix backend
head_size=64,
tmix_backend="auto",
init_state_wkv=False,
# Trainer model configs
dropout_rate=0.0,
# Torch device and dtype
device=None,
dtype=None,
# Tokenizer related settings in HF configuration
bos_token_id=0,
eos_token_id=0,
tie_word_embeddings=False,
########################################
**kwargs,
):
# Normalize dtype if torch_dtype is set within kwargs
if dtype is None and "torch_dtype" in kwargs:
dtype = kwargs["torch_dtype"]
self.vocab_size = vocab_size
self.num_hidden_layers = num_hidden_layers
self.hidden_size = hidden_size
self.hidden_size_att = hidden_size_att
self.hidden_size_ffn = hidden_size_ffn
self.head_size = head_size
self.tmix_backend = tmix_backend
self.init_state_wkv = init_state_wkv
self.device = device
self.dtype = dtype
self.dropout_rate = dropout_rate
# Forward to the HF PretrainedConfig
super().__init__(
tie_word_embeddings=tie_word_embeddings,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs
)
@staticmethod
def from_model_state_dict(state_dict: dict, **kwargs):
goose_config = RWKV7GooseConfigMap.from_model_state_dict(state_dict)
# Join dictionary with **goose_config.__dict__ and **kwargs
return RWKV7Config(**{**goose_config.__dict__, **kwargs}) |