966436403e5e927e3e7d5b389dad6ef06aaa7e03,fairseq/sequence_generator.py,EnsembleModel,__init__,#EnsembleModel#Any#,638

Before Change

        self.incremental_states = torch.jit.annotate(
            List[Dict[str, Dict[str, Optional[Tensor]]]],
            [
                torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {})
                for i in range(self.models_size)
            ],
        )
        self.has_incremental: bool = False

After Change

        self.models = torch.nn.ModuleList(models)
        self.incremental_states = None
        if all(hasattr(m, "decoder") and isinstance(m.decoder, FairseqIncrementalDecoder) for m in models):
            self.incremental_states = {m: {} for m in models}

    def has_encoder(self):
        return hasattr(self.models[0], "encoder")

Instances