ValueError: The following `model_kwargs` are not used by the model: ['length']

@zzxslp

Change these line at https://github.com/salesforce/BLIP/blob/main/models/med.py#L932 as following:

from

    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids, 
            "attention_mask": attention_mask, 
            "past_key_values": past,
            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
            "is_decoder": True,
        }

to

    def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, **model_kwargs):
        input_shape = input_ids.shape
        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
        if attention_mask is None:
            attention_mask = input_ids.new_ones(input_shape)

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "input_ids": input_ids, 
            "attention_mask": attention_mask, 
            "past_key_values": past,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": encoder_attention_mask,
            "is_decoder": True,
        }

Why: https://github.com/huggingface/transformers/blob/v4.23.1/src/transformers/generation_utils.py#L899

Well, in that case I just downloaded the model. You can usually initialize a random tiny model using a different configuration. Could you tell me why you need to set return_length=True. This has the effect of adding length to the list of inputs, and is not useful when generating. This argument is mostly used when training a fast tokenizer.