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llama : add llama_model_is_recurrent to simplify figuring that out
This will make it easier to more cleanly support RWKV-v6 and Mamba-2.
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@ -508,6 +508,9 @@ extern "C" {
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// to the decoder to start generating output sequence. For other models, it returns -1.
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LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
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// Returns true if the model is recurrent (like Mamba, RWKV, etc.)
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LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model);
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// Returns 0 on success
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LLAMA_API uint32_t llama_model_quantize(
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const char * fname_inp,
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@ -3292,8 +3292,7 @@ static bool llama_kv_cache_init(
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cache.has_shift = false;
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// TODO: find a nicer way to add other recurrent model architectures
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cache.recurrent = model.arch == LLM_ARCH_MAMBA;
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cache.recurrent = llama_model_is_recurrent(&model);
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cache.v_trans = !cache.recurrent && !cparams.flash_attn;
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cache.head = 0;
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@ -17235,7 +17234,7 @@ struct llama_context * llama_new_context_with_model(
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ggml_type type_v = params.type_v;
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// Mamba only needs a constant number of KV cache cells per sequence
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if (model->arch == LLM_ARCH_MAMBA) {
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if (llama_model_is_recurrent(model)) {
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// Mamba needs at least as many KV cells as there are sequences kept at any time
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kv_size = std::max((uint32_t) 1, params.n_seq_max);
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// it's probably best to keep as much precision as possible for the states
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@ -17709,6 +17708,13 @@ llama_token llama_model_decoder_start_token(const struct llama_model * model) {
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return model->hparams.dec_start_token_id;
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}
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bool llama_model_is_recurrent(const struct llama_model * model) {
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switch (model->arch) {
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case LLM_ARCH_MAMBA: return true;
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default: return false;
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}
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}
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uint32_t llama_model_quantize(
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const char * fname_inp,
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const char * fname_out,
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