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