Delete configuration_codet5p.py

configuration_codet5p.py

@@ -1,113 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Salesforce authors, The EleutherAI, and HuggingFace Teams. All rights reserved.
-
-""" CodeT5+ model configuration"""
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-import copy
-
-logger = logging.get_logger(__name__)
-
-
-# Adapted from transformers.models.codegen.configuration_codegen.CodeGenConfig
-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
-        )
-
-
-# Adapted from transformers.models.encoder_decoder.configuration_encoder_decoder.EncoderDecoderConfig
-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