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