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""" Gemma model configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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GEMMA_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"google/gemma-2b": "https://huggingface.co/google/gemma-2b/resolve/main/config.json", |
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} |
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class GemmaConfig(PretrainedConfig): |
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model_type = "gemma" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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def __init__( |
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self, |
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vocab_size=51200, |
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hidden_size=2048, |
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intermediate_size=8192, |
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num_hidden_layers=24, |
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num_attention_heads=32, |
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num_key_value_heads=None, |
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resid_pdrop=0.0, |
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embd_pdrop=0.0, |
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attention_dropout=0.0, |
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hidden_act="gelu_new", |
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max_position_embeddings=2048, |
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initializer_range=0.02, |
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layer_norm_eps=1e-5, |
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use_cache=True, |
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tie_word_embeddings=False, |
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rope_theta=10000.0, |
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rope_scaling=None, |
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partial_rotary_factor=0.5, |
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qk_layernorm=False, |
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bos_token_id=1, |
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eos_token_id=2, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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if num_key_value_heads is None: |
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num_key_value_heads = num_attention_heads |
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self.num_key_value_heads = num_key_value_heads |
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self.resid_pdrop = resid_pdrop |
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self.embd_pdrop = embd_pdrop |
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self.attention_dropout = attention_dropout |
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self.hidden_act = hidden_act |
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self.max_position_embeddings = max_position_embeddings |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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self.use_cache = use_cache |
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self.rope_theta = rope_theta |
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self.rope_scaling = rope_scaling |
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self.partial_rotary_factor = partial_rotary_factor |
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self.qk_layernorm = qk_layernorm |
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self._rope_scaling_validation() |
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super().__init__( |
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bos_token_id=bos_token_id, |
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eos_token_id=eos_token_id, |
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tie_word_embeddings=tie_word_embeddings, |
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**kwargs, |
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) |
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def _rope_scaling_validation(self): |
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""" |
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Validate the `rope_scaling` configuration. |
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""" |
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if self.rope_scaling is None: |
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return |
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: |
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raise ValueError( |
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " |
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f"got {self.rope_scaling}" |
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) |
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rope_scaling_type = self.rope_scaling.get("type", None) |
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rope_scaling_factor = self.rope_scaling.get("factor", None) |
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: |
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raise ValueError( |
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" |
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) |
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: |
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") |
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from typing import Union |
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from transformers import PretrainedConfig |
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import os |
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class SigLipVisionConfig(PretrainedConfig): |
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model_type = "siglip_vision_model" |
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def __init__( |
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self, |
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hidden_size=1152, |
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image_mean=(0.5, 0.5, 0.5), |
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intermediate_size=4304, |
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num_hidden_layers=27, |
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num_attention_heads=16, |
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num_channels=3, |
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image_size=384, |
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patch_size=14, |
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hidden_act="gelu_pytorch_tanh", |
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layer_norm_eps=1e-6, |
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attention_dropout=0.0, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.hidden_size = hidden_size |
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self.intermediate_size = intermediate_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.num_channels = num_channels |
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self.patch_size = patch_size |
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self.image_size = image_size |
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self.attention_dropout = attention_dropout |
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self.layer_norm_eps = layer_norm_eps |
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self.hidden_act = hidden_act |
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self.image_mean = image_mean |
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@classmethod |
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": |
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cls._set_token_in_kwargs(kwargs) |
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) |
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if config_dict.get("model_type") == "siglip": |
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config_dict = config_dict["vision_config"] |
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: |
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logger.warning( |
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " |
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." |
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) |
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return cls.from_dict(config_dict, **kwargs) |
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class CeruleGemmaConfig(GemmaConfig): |
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model_type = "cerule-gemma" |
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def __init__(self, **kwargs): |
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self.gemma_config = GemmaConfig(**kwargs) |
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super().__init__(**kwargs) |
