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| from transformers import PretrainedConfig | |
| 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, | |
| 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._rope_scaling_validation() | |
| super().__init__( | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation | |
| 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 MoondreamConfig(PretrainedConfig): | |
| model_type = "moondream1" | |
| def __init__(self, **kwargs): | |
| self.text_config = PhiConfig(**kwargs.pop("text_config", {})) | |
| super().__init__(**kwargs) | |