| from transformers.configuration_utils import PretrainedConfig |
| from transformers.modeling_rope_utils import rope_config_validation |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| class CoDAConfig(PretrainedConfig): |
| model_type = "CoDA" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=151936, |
| head_dim=128, |
| hidden_act="silu", |
| hidden_size=2048, |
| intermediate_size=6144, |
| num_attention_heads=16, |
| num_hidden_layers=28, |
| num_key_value_heads=8, |
| max_position_embeddings=40960, |
| initializer_range=0.02, |
| use_cache=True, |
| use_sliding_window=False, |
| tie_word_embeddings=True, |
| rms_norm_eps=1e-6, |
| rope_scaling=None, |
| rope_theta=1000000, |
| sliding_window=None, |
| max_window_layers=28, |
| attention_bias=False, |
| attention_dropout=0.0, |
| bos_token_id=151643, |
| eos_token_id=151645, |
| pad_token_id=151643, |
| mask_token_id=151669, |
| attention_kernel="flash_attention", |
| prefix_probability=0, |
| truncate_probability=0, |
| block_masking_probability=[0.25, 0.5, 0.5, 0.75, 0.25], |
| mask_block_sizes=[4, 8, 16, 32], |
| sampling_eps=[0.001, 0.25, 0.5, 0.25, 0.001], |
| **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 |
| self.use_sliding_window = use_sliding_window |
| self.sliding_window = sliding_window if use_sliding_window else None |
| self.max_window_layers = max_window_layers |
|
|
| |
| 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.attention_dropout = attention_dropout |
| |
| |
| if self.rope_scaling is not None and "type" in self.rope_scaling: |
| self.rope_scaling["rope_type"] = self.rope_scaling["type"] |
| rope_config_validation(self) |
|
|
| self.head_dim = head_dim |
| self.attention_bias = attention_bias |
| self.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
| self.attention_kernel = attention_kernel |
| self.prefix_probability = prefix_probability |
| self.truncate_probability = truncate_probability |
| self.block_masking_probability = block_masking_probability |
| self.mask_block_sizes = mask_block_sizes |
| self.sampling_eps = sampling_eps |
|
|
| super().__init__( |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
| self.mask_token_id = mask_token_id |
| self.pad_token_id = pad_token_id |
|
|
