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[Deprecated] Size mismatch when setting knowledge_usage to separate

See original GitHub issue

For the evaluation, I receive the following error:

...
  File "/home/pouramini/UnifiedSKG/utils/trainer.py", line 298, in prediction_step
    **gen_kwargs,
  File "/home/pouramini/UnifiedSKG/models/unified/prefixtuning.py", line 289, in generate
    bsz=bsz, sample_size=kwargs['num_beams'], description=description_representation, knowledge=knowledge_representation,
  File "/home/pouramini/UnifiedSKG/models/unified/prefixtuning.py", line 120, in get_prompt
    past_key_values = torch.cat([past_key_values, self.knowledge_trans(knowledge)], dim=1)
RuntimeError: Sizes of tensors must match except in dimension 0. Got 4 and 16 (The offending index is 0)

It seems when you use num_beam greater than 1 and it’s used as sample_size in get_prompt method and is multiplied to batch_size, the mismatch occurs.

    def get_prompt(self, bsz=None, sample_size=1, description=None, knowledge=None):
        old_bsz = bsz
        bsz = bsz * sample_size
        input_tokens = self.input_tokens.unsqueeze(0).expand(bsz, -1)
        temp_control = self.wte(input_tokens)
        if description is not None:
            temp_control = temp_control + description.repeat_interleave(sample_size, dim=0).unsqueeze(1)
        past_key_values = self.control_trans(temp_control)  # bsz, seqlen, layer*emb
        if knowledge is not None:
            past_key_values = torch.cat([past_key_values, self.knowledge_trans(knowledge)], dim=1)