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llama : refactor llama_context, llama_kv_cache, llm_build_context (#12181)
* llama : refactor llama_context, llama_kv_cache, llm_build_context ggml-ci * graph : don't mutate the KV cache during defrag ggml-ci * context : reduce virtuals + remove test function ggml-ci * context : move interface implementation to source file + factory ggml-ci * graph : move KV cache build functions to llama_context impl ggml-ci * graph : remove model reference from build_pooling ggml-ci * graph : remove llama_model reference ggml-ci * kv_cache : provide rope factors ggml-ci * graph : rework inputs to use only unique_ptr, remove attn input abstraction ggml-ci * context : remove llama_context_i abstraction ggml-ci * context : clean-up ggml-ci * graph : clean-up ggml-ci * llama : remove redundant keywords (struct, enum) ggml-ci * model : adapt gemma3 ggml-ci * graph : restore same attention ops as on master ggml-ci * llama : remove TODO + fix indent ggml-ci
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@ -210,7 +210,7 @@ actor LlamaContext {
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}
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batch.logits[Int(batch.n_tokens) - 1] = 1 // true
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llama_kv_cache_clear(context)
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llama_kv_self_clear(context)
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let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000;
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@ -223,7 +223,7 @@ actor LlamaContext {
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// bench text generation
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llama_kv_cache_clear(context)
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llama_kv_self_clear(context)
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let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000;
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@ -242,7 +242,7 @@ actor LlamaContext {
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let t_tg_end = DispatchTime.now().uptimeNanoseconds / 1000;
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llama_kv_cache_clear(context)
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llama_kv_self_clear(context)
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let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
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let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
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@ -292,7 +292,7 @@ actor LlamaContext {
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func clear() {
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tokens_list.removeAll()
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temporary_invalid_cchars.removeAll()
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llama_kv_cache_clear(context)
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llama_kv_self_clear(context)
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}
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private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
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