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