033f2625feff5428a243df1c1220830739dc7d3c,pytext/torchscript/module.py,ScriptPyTextVariableSizeEmbeddingModule,make_prediction,#ScriptPyTextVariableSizeEmbeddingModule#Any#,521

Before Change

        if argno == TEXTS:
            flat_texts: List[str] = []

            for i in range(batchsize):
                batch_element = batch[i][0]
                if batch_element is not None:
                    flat_texts.extend(batch_element)
                    client_batch.append(len(batch_element))
                else:
                    // At present, we abort the entire batch if
                    // any batch element is malformed.
                    //
                    // Possible refinement:
                    // we can skip malformed requests,
                    // and return a list plus an indiction that one or more
                    // batch elements (and which ones) were malformed
                    raise RuntimeError("Malformed request.")

            flat_result: List[torch.Tensor] = self.forward(
                texts=flat_texts,
                multi_texts=None,
                tokens=None,

After Change

                    // batch elements (and which ones) were malformed
                    raise RuntimeError("(VE) Malformed request.")

            if len(flat_texts) == 0:
                raise RuntimeError("This is not good. Empty request batch.")

            flat_result: List[torch.Tensor] = self.forward(
                texts=flat_texts,
                multi_texts=None,
                tokens=None,

Instances