| | 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)}" |
| | ) |
| |
|
