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import os |
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import sys |
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from transformers import PretrainedConfig, PreTrainedModel |
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from ultra.models import Ultra |
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from ultra.datasets import WN18RR, CoDExSmall, FB15k237, FB15k237Inductive |
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from ultra.eval import test |
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class UltraConfig(PretrainedConfig): |
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model_type = "ultra" |
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auto_map = { |
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"AutoConfig": "modeling.UltraConfig", |
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"AutoModel": "modeling.UltraForKnowledgeGraphReasoning", |
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} |
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def __init__( |
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self, |
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relation_model_layers: int = 6, |
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relation_model_dim: int = 64, |
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entity_model_layers: int = 6, |
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entity_model_dim: int = 64, |
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**kwargs): |
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self.relation_model_cfg = dict( |
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input_dim=relation_model_dim, |
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hidden_dims=[relation_model_dim]*relation_model_layers, |
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message_func="distmult", |
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aggregate_func="sum", |
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short_cut=True, |
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layer_norm=True |
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) |
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self.entity_model_cfg = dict( |
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input_dim=entity_model_dim, |
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hidden_dims=[entity_model_dim]*entity_model_layers, |
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message_func="distmult", |
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aggregate_func="sum", |
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short_cut=True, |
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layer_norm=True |
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) |
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super().__init__(**kwargs) |
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class UltraForKnowledgeGraphReasoning(PreTrainedModel): |
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config_class = UltraConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.model = Ultra( |
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rel_model_cfg=config.relation_model_cfg, |
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entity_model_cfg=config.entity_model_cfg, |
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) |
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def forward(self, data, batch): |
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return self.model.forward(data, batch) |
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if __name__ == "__main__": |
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model = UltraForKnowledgeGraphReasoning.from_pretrained("mgalkin/ultra_3g") |
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dataset = CoDExSmall(root="./datasets/") |
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test(model, mode="test", dataset=dataset, gpus=None) |
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