| | """ EvaByte configuration""" |
| |
|
| | from transformers.configuration_utils import PretrainedConfig |
| |
|
| | class EvaByteConfig(PretrainedConfig): |
| | model_type = "evabyte" |
| | keys_to_ignore_at_inference = ["past_key_values"] |
| |
|
| | def __init__( |
| | self, |
| | vocab_size=320, |
| | hidden_size=4096, |
| | intermediate_size=11008, |
| | num_hidden_layers=32, |
| | num_attention_heads=32, |
| | num_key_value_heads=None, |
| | hidden_act="silu", |
| | max_position_embeddings=2048, |
| | initializer_range=0.02, |
| | rms_norm_eps=1e-6, |
| | use_cache=True, |
| | pad_token_id=None, |
| | bos_token_id=1, |
| | eos_token_id=2, |
| | tie_word_embeddings=False, |
| | rope_theta=10000.0, |
| | rope_scaling=None, |
| | attention_bias=False, |
| | attention_dropout=0.0, |
| | norm_add_unit_offset=False, |
| | init_fn="mitchell", |
| | init_std=0.006, |
| | init_cutoff_factor=None, |
| | attention_class="mha", |
| | window_size=512, |
| | num_chunks=None, |
| | chunk_size=256, |
| | **kwargs, |
| | ): |
| | self.vocab_size = vocab_size |
| | self.max_position_embeddings = max_position_embeddings |
| | 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.hidden_act = hidden_act |
| | self.initializer_range = initializer_range |
| | self.rms_norm_eps = rms_norm_eps |
| | self.use_cache = use_cache |
| | self.rope_theta = rope_theta |
| | self.rope_scaling = rope_scaling |
| | self._rope_scaling_validation() |
| | self.attention_bias = attention_bias |
| | self.attention_dropout = attention_dropout |
| |
|
| | self.norm_add_unit_offset = norm_add_unit_offset |
| | self.init_fn = init_fn |
| | self.init_std = init_std |
| | self.init_cutoff_factor = init_cutoff_factor |
| |
|
| | |
| | self.attention_class = attention_class |
| | self.window_size = window_size |
| | self.num_chunks = num_chunks |
| | self.chunk_size = chunk_size |
| |
|
| | super().__init__( |
| | pad_token_id=pad_token_id, |
| | 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 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}") |
| |
|
