| |
| |
|
|
| """ CodeT5+ model configuration""" |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
| import copy |
|
|
| logger = logging.get_logger(__name__) |
|
|
|
|
| |
| class CodeT5pModuleConfig(PretrainedConfig): |
| model_type = "codet5p_module" |
| attribute_map = { |
| "max_position_embeddings": "n_positions", |
| "hidden_size": "n_embd", |
| "num_attention_heads": "n_head", |
| "num_hidden_layers": "n_layer", |
| } |
|
|
| def __init__( |
| self, |
| vocab_size=50400, |
| n_positions=2048, |
| n_ctx=2048, |
| n_embd=4096, |
| n_layer=28, |
| n_head=16, |
| rotary_dim=64, |
| n_inner=None, |
| activation_function="gelu_new", |
| resid_pdrop=0.0, |
| embd_pdrop=0.0, |
| attn_pdrop=0.0, |
| layer_norm_epsilon=1e-5, |
| initializer_range=0.02, |
| scale_attn_weights=True, |
| use_cache=True, |
| bos_token_id=50256, |
| eos_token_id=50256, |
| tie_word_embeddings=False, |
| **kwargs |
| ): |
| self.vocab_size = vocab_size |
| self.n_ctx = n_ctx |
| self.n_positions = n_positions |
| self.n_embd = n_embd |
| self.n_layer = n_layer |
| self.n_head = n_head |
| self.n_inner = n_inner |
| self.rotary_dim = rotary_dim |
| self.activation_function = activation_function |
| self.resid_pdrop = resid_pdrop |
| self.embd_pdrop = embd_pdrop |
| self.attn_pdrop = attn_pdrop |
| self.layer_norm_epsilon = layer_norm_epsilon |
| self.initializer_range = initializer_range |
| self.scale_attn_weights = scale_attn_weights |
| self.use_cache = use_cache |
|
|
| self.bos_token_id = bos_token_id |
| self.eos_token_id = eos_token_id |
|
|
| super().__init__( |
| bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs |
| ) |
|
|
|
|
| |
| class CodeT5pConfig(PretrainedConfig): |
| model_type = "codet5p" |
| is_composition = True |
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
| assert ( |
| "encoder" in kwargs and "decoder" in kwargs |
| ), "Config has to be initialized with encoder and decoder config" |
| encoder_config = kwargs.pop("encoder") |
| decoder_config = kwargs.pop("decoder") |
| encoder_model_type = encoder_config.pop("model_type") |
| decoder_model_type = decoder_config.pop("model_type") |
|
|
| if encoder_model_type != decoder_model_type: |
| logger.warning("Encoder and decoder model types are different") |
|
|
| self.encoder = CodeT5pModuleConfig(**encoder_config) |
| self.decoder = CodeT5pModuleConfig(**decoder_config) |
| self.is_encoder_decoder = True |
|
|
| @classmethod |
| def from_encoder_decoder_configs( |
| cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs |
| ) -> PretrainedConfig: |
| logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") |
| decoder_config.is_decoder = True |
| decoder_config.add_cross_attention = True |
|
|
| return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) |
|
|
| def to_dict(self): |
| """ |
| Serializes this instance to a Python dictionary. Override the default *to_dict()* from *PretrainedConfig*. |
| |
| Returns: |
| `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, |
| """ |
| output = copy.deepcopy(self.__dict__) |
| output["encoder"] = self.encoder.to_dict() |
| output["decoder"] = self.decoder.to_dict() |
| output["model_type"] = self.__class__.model_type |
| return output |
|
|
