| from transformers import PretrainedConfig
|
|
|
|
|
| class PhiConfig(PretrainedConfig):
|
| model_type = "phi"
|
| keys_to_ignore_at_inference = ["past_key_values"]
|
|
|
| def __init__(
|
| 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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| bos_token_id=1,
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| eos_token_id=2,
|
| **kwargs,
|
| ):
|
| self.vocab_size = vocab_size
|
| 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
|
|
|
| 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
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| self.max_position_embeddings = max_position_embeddings
|
| self.initializer_range = initializer_range
|
| 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
|
| 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.text_config = PhiConfig(**kwargs.pop("text_config", {}))
|
| super().__init__(**kwargs)
|
|
|
