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https://github.com/ggerganov/whisper.cpp.git
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talk-llama : sync llama.cpp
ggml-ci
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@ -18,6 +18,9 @@ struct llama_kv_cache;
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class llama_io_read_i;
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class llama_io_write_i;
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class llama_memory_i;
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class llama_memory_state_i;
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struct llama_context {
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// init scheduler and compute buffers, reserve worst-case graphs
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llama_context(
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@ -47,7 +50,9 @@ struct llama_context {
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llama_kv_cache * get_kv_self();
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const llama_kv_cache * get_kv_self() const;
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void kv_self_update();
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// return true of the KV cache was updated
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// TODO: remove
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bool kv_self_update();
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enum llama_pooling_type pooling_type() const;
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@ -88,6 +93,16 @@ struct llama_context {
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int32_t il_start,
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int32_t il_end);
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// process a single ubatch with a specific graph type
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// if memory_state is provided, it will be applied first to the context's memory
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// ret contains the status of the graph computation
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// returns nullptr only if ret != GGML_STATUS_SUCCESS
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llm_graph_result_ptr process_ubatch(
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const llama_ubatch & ubatch,
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llm_graph_type gtype,
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llama_memory_state_i * mstate,
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ggml_status & ret);
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int encode(llama_batch & inp_batch);
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int decode(llama_batch & inp_batch);
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@ -180,16 +195,18 @@ public:
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ggml_cgraph * graph_init();
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// returns the result of ggml_backend_sched_graph_compute_async execution
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ggml_status graph_compute(
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ggml_cgraph * gf,
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bool batched);
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ggml_status graph_compute(ggml_cgraph * gf, bool batched);
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// reserve a graph with a dummy ubatch of the specified size
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ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate);
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private:
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llm_graph_result_ptr graph_build(
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ggml_context * ctx,
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ggml_cgraph * gf,
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const llama_ubatch & ubatch,
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llm_graph_type gtype);
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ggml_context * ctx,
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ggml_cgraph * gf,
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const llama_ubatch & ubatch,
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llm_graph_type gtype,
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const llama_memory_state_i * mstate);
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llm_graph_cb graph_get_cb() const;
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