| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
|
|
| class CodeFuseCGESmallConfig(PretrainedConfig): |
| model_type = "phi3" |
| keys_to_ignore_at_inference = ["past_key_values"] |
|
|
| def __init__( |
| self, |
| vocab_size=32064, |
| hidden_size=3072, |
| intermediate_size=8192, |
| num_hidden_layers=32, |
| num_attention_heads=32, |
| num_key_value_heads=None, |
| resid_pdrop=0.0, |
| embd_pdrop=0.0, |
| attention_dropout=0.0, |
| hidden_act="silu", |
| max_position_embeddings=4096, |
| original_max_position_embeddings=4096, |
| initializer_range=0.02, |
| rms_norm_eps=1e-5, |
| use_cache=True, |
| tie_word_embeddings=False, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| bos_token_id=1, |
| eos_token_id=32000, |
| pad_token_id=32000, |
| sliding_window=None, |
| embedding_method="pma", |
| inf_seq_length=1024, |
| padding_side="right", |
| compress_dim=1024, |
| keep_max_layer=32, |
| pma_num_heads=32, |
| pma_ln=True, |
| pma_norm=False, |
| pma_norm_mode="post_normal", |
| **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.original_max_position_embeddings = original_max_position_embeddings |
| 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_adjustment() |
| self._rope_scaling_validation() |
| self.sliding_window = sliding_window |
|
|
| self.embedding_method = embedding_method |
| self.inf_seq_length = inf_seq_length |
| self.padding_side = padding_side |
| self.compress_dim = compress_dim |
| self.keep_max_layer = keep_max_layer |
| self.pma_num_heads = pma_num_heads |
| self.pma_ln = pma_ln |
| self.pma_norm = pma_norm |
| self.pma_norm_mode = pma_norm_mode |
|
|
| super().__init__( |
| bos_token_id=bos_token_id, |
| eos_token_id=eos_token_id, |
| pad_token_id=pad_token_id, |
| tie_word_embeddings=tie_word_embeddings, |
| **kwargs, |
| ) |
|
|
| def _rope_scaling_adjustment(self): |
| """ |
| Adjust the `type` of the `rope_scaling` configuration for backward compatibility. |
| """ |
| if self.rope_scaling is None: |
| return |
|
|
| rope_scaling_type = self.rope_scaling.get("type", None) |
|
|
| |
| if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]: |
| self.rope_scaling["type"] = "longrope" |
|
|
| 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) != 3: |
| raise ValueError( |
| "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, " |
| f"got {self.rope_scaling}" |
| ) |
| rope_scaling_type = self.rope_scaling.get("type", None) |
| rope_scaling_short_factor = self.rope_scaling.get("short_factor", None) |
| rope_scaling_long_factor = self.rope_scaling.get("long_factor", None) |
| if rope_scaling_type is None or rope_scaling_type not in ["longrope"]: |
| raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}") |
| if not ( |
| isinstance(rope_scaling_short_factor, list) |
| and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor) |
| ): |
| raise ValueError( |
| f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}" |
| ) |
| if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2: |
| raise ValueError( |
| f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}" |
| ) |
| if not ( |
| isinstance(rope_scaling_long_factor, list) |
| and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor) |
| ): |
| raise ValueError( |
| f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}" |
| ) |
| if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2: |
| raise ValueError( |
| f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}" |
| ) |
|
|
