Upload configuration_codet5p.py

configuration_codet5p.py

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+# 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