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""" CodeT5+ embedding 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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logger = logging.get_logger(__name__) |
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class CodeT5pEmbeddingConfig(PretrainedConfig): |
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model_type = "codet5p_embedding" |
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keys_to_ignore_at_inference = ["past_key_values"] |
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attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} |
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def __init__( |
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self, |
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vocab_size=32103, |
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d_model=768, |
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embed_dim=256, |
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d_kv=64, |
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d_ff=3072, |
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num_layers=12, |
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num_heads=12, |
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relative_attention_num_buckets=32, |
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relative_attention_max_distance=128, |
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dropout_rate=0.1, |
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layer_norm_epsilon=1e-6, |
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initializer_factor=1.0, |
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feed_forward_proj="relu", |
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is_encoder_decoder=False, |
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use_cache=True, |
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pad_token_id=0, |
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eos_token_id=2, |
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**kwargs |
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): |
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self.vocab_size = vocab_size |
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self.d_model = d_model |
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self.embed_dim = embed_dim |
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self.d_kv = d_kv |
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self.d_ff = d_ff |
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self.num_layers = num_layers |
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self.num_heads = num_heads |
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self.relative_attention_num_buckets = relative_attention_num_buckets |
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self.relative_attention_max_distance = relative_attention_max_distance |
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self.dropout_rate = dropout_rate |
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self.layer_norm_epsilon = layer_norm_epsilon |
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self.initializer_factor = initializer_factor |
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self.feed_forward_proj = feed_forward_proj |
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self.use_cache = use_cache |
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act_info = self.feed_forward_proj.split("-") |
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self.dense_act_fn = act_info[-1] |
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self.is_gated_act = act_info[0] == "gated" |
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if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: |
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raise ValueError( |
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f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." |
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"Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " |
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"'gated-gelu' or 'relu'" |
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) |
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if feed_forward_proj == "gated-gelu": |
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self.dense_act_fn = "gelu_new" |
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super().__init__( |
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pad_token_id=pad_token_id, |
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eos_token_id=eos_token_id, |
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is_encoder_decoder=is_encoder_decoder, |
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**kwargs, |
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) |
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