why does ORTModelForCausalLM assume new input length is 1 when past_key_values is passed

[cyh-ustc]

optimum/optimum/onnxruntime/modeling_decoder.py

Line 649 in c55f882

remove_prefix_length = input_ids.shape[1] - 1

    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            # Some generation methods already pass only the last input ID
            if input_ids.shape[1] > past_length:
                remove_prefix_length = past_length
            else:
                # Default to old behavior: keep only final ID
                remove_prefix_length = input_ids.shape[1] - 1
            input_ids = input_ids[:, remove_prefix_length:]

while in non-onnx modeling, it's not.

https://github.com/huggingface/transformers/blob/a98c41798cf6ed99e1ff17e3792d6e06a2ff2ff3/src/transformers/models/mistral/modeling_mistral.py#L1217

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]