| 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, |
| 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 MoondreamConfig(PretrainedConfig): |
| model_type = "moondream1" |
|
|
| def __init__(self, **kwargs): |
| self.phi_config = PhiConfig(**kwargs) |
| super().__init__(**kwargs) |
|
|
