fill prompt for sampler analysis with real tokens in VLM pipeline #1247
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| auto chat_history = m_inputs_embedder->get_tokenized_chat_history(); | ||
| size_t chat_history_size = std::max(chat_history.get_shape().at(1), history_size + inputs_embeds_size); | ||
| ov::Tensor prompt_ids(ov::element::i64, { chat_history_size }); | ||
| std::fill_n(prompt_ids.data<int64_t>(), prompt_ids.get_size(), 1); |
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why 1 is used as default value? maybe pad_token ?
| std::fill_n(prompt_ids.data<int64_t>(), prompt_ids.get_size(), 0); | ||
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| auto chat_history = m_inputs_embedder->get_tokenized_chat_history(); | ||
| size_t chat_history_size = std::max(chat_history.get_shape().at(1), history_size + inputs_embeds_size); |
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looks like we have the same case as for LLMs, when decode ( encode ( X ) ) provides smaller value than X ?
in this case we need to partially re-compute the history.
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in general, I would consider merging VLM and LLM pipelines generate functions to keep all this magic with history in one place.
Or at least to create helper function similar to get_lm_encoded_results
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