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model : add PLaMo-2 support (#14560)
* Add PLaMo-2 model using hybrid memory module * Fix z shape * Add cmath to include from llama-vocab.h * Explicitly dequantize normalization weights before RoPE apply * Revert unnecessary cast because the problem can be solved by excluding attn_k, attn_q when quantizing * Use ATTN_K/Q_NORM for k,q weights to prevent quantization * Remove SSM_BCDT that is not used from anywhere * Do not duplicate embedding weights for output.weight * Fix tokenizer encoding problem for multibyte strings * Apply suggestion from @CISC Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Use LLM_FFN_SWIGLU instead of splitting ffn_gate and ffn_up * Remove unnecessary part for Grouped Query Attention * Fix how to load special token id to gguf * Remove unused tensor mapping * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Remove llama_vocab_plamo2 class and replace it with llm_tokenizer_plamo2_session to follow the other tokenizer implementations * Update src/llama-vocab.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Fix plamo2 tokenizer session to prevent multiple calls of build() --------- Co-authored-by: Francis Couture-Harpin <git@compilade.net> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -71,12 +71,13 @@ extern "C" {
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typedef int32_t llama_seq_id;
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enum llama_vocab_type {
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LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
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LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
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LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
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LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
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LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
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LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
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LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
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LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
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LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
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LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
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LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram
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LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization
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LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming
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};
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enum llama_rope_type {
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