| rom transformers.configuration_utils import PretrainedConfig | |
| from transformers.utils import logging | |
| from transformers import SiglipVisionConfig | |
| logger = logging.get_logger(__name__) | |
| class PhiConfig(PretrainedConfig): | |
| model_type = "phi" | |
| 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}" | |
| ) | |
| class LlavaConfig(PretrainedConfig): | |
| model_type = "HelpingAI-V" | |
| is_composition = False | |
| def __init__( | |
| self, | |
| text_config=None, | |
| vision_config=None, | |
| ignore_index=-100, | |
| image_token_index=50297, | |
| projector_hidden_act="gelu", | |
| projector_tokens_num=1, | |
| vocab_size=51200, | |
| **kwargs, | |
| ): | |
| self.ignore_index = ignore_index | |
| self.image_token_index = image_token_index | |
| self.projector_hidden_act = projector_hidden_act | |
| self.projector_tokens_num = projector_tokens_num | |
| self.vocab_size = vocab_size | |
| self.text_config = text_config | |
| if isinstance(self.text_config, dict): | |
| text_config["model_type"] = ( | |
| text_config["model_type"] if "model_type" in text_config else "phi" | |
| ) | |
| self.text_config = PhiConfig(**text_config) | |
| self.vocab_size = self.text_config.vocab_size | |
| self.vision_config = vision_config | |
| if isinstance(self.vision_config, dict): | |
| self.vision_config = SiglipVisionConfig(**vision_config) | |
| self.vision_embed_dim = self.vision_config.hidden_size | |
| super().__init__(**kwargs) | |