PatrickHaller
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Upload configuration_xlstm.py with huggingface_hub
Browse files- configuration_xlstm.py +97 -0
configuration_xlstm.py
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import json
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from typing import Any, Dict, Optional
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from dacite import Config as DaciteConfig
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from dacite import from_dict
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from omegaconf import OmegaConf
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from transformers.configuration_utils import PretrainedConfig
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from xlstm import xLSTMLMModelConfig
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# from .config_presets import xlstm_cfg_map
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class xLSTMConfig(PretrainedConfig):
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"""XLSTM configuration class.
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We seperate the specific xLSTM model configuration
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from the rest due to the heavy nesting of the configuration.
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"""
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model_type = "xlstm"
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def __init__(
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self, vocab_size: int = 32000, config: Optional[Dict[str, Any]] = None, **kwargs
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):
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super().__init__(**kwargs)
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cfg = OmegaConf.create(config)
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cfg["vocab_size"] = vocab_size
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for key, value in kwargs.items():
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cfg[key] = value
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self._xlstm_config = cfg
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self.vocab_size = vocab_size
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self.embedding_dim = cfg.get("embedding_dim")
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self.context_length = cfg.get("context_length")
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def to_xlstm_config(self):
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return from_dict(
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data_class=xLSTMLMModelConfig,
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data=OmegaConf.to_container(self._xlstm_config),
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config=DaciteConfig(strict=True),
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)
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def to_dict(self) -> Dict[str, Any]:
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"""
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Converts the configuration to a dictionary for serialization.
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"""
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output = super().to_dict()
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output["_xlstm_config"] = OmegaConf.to_container(
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self._xlstm_config, resolve=True
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)
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relevant_keys = [
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"vocab_size",
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"embedding_dim",
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"context_length",
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"torch_dtype",
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"_xlstm_config",
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"transformers_version",
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"architectures",
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"model_type",
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]
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output_ = output.copy()
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for key in output.keys():
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if key not in relevant_keys:
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output_.pop(key)
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return output_
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@classmethod
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def from_dict(cls, config_dict: Dict[str, Any], **kwargs):
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"""
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Creates a configuration instance from a dictionary.
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"""
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xlstm_config = config_dict.pop("_xlstm_config")
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vocab_size = config_dict.pop("vocab_size")
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config = cls(vocab_size=vocab_size, config=xlstm_config)
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if "auto_map" in config_dict and config_dict["auto_map"]:
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setattr(config, "auto_map", config_dict.pop("auto_map"))
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# breakpoint()
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# config.xlstm_config = xlstm_config
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if "return_unused_kwargs" in kwargs and kwargs["return_unused_kwargs"]:
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return config, {}
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return config
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def to_json_string(self, *args, **kwargs) -> str:
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"""
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Serializes the instance to a JSON string.
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"""
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return json.dumps(self.to_dict(), indent=2)
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@classmethod
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def from_json_string(cls, json_string: str):
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"""
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Deserializes the instance from a JSON string.
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"""
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config_dict = json.loads(json_string)
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return cls.from_dict(config_dict)
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