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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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@ -200,18 +200,6 @@ public:
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const llama_kv_cache_recurrent_state * kv_state;
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};
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class llm_graph_input_s_mask : public llm_graph_input_i {
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public:
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llm_graph_input_s_mask(const llama_kv_cache_recurrent_state * kv_state) : kv_state(kv_state) {}
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virtual ~llm_graph_input_s_mask() = default;
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void set_input(const llama_ubatch * ubatch) override;
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ggml_tensor * s_mask; // F32 [1, n_kv]
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const llama_kv_cache_recurrent_state * kv_state;
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};
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class llm_graph_input_cross_embd : public llm_graph_input_i {
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public:
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llm_graph_input_cross_embd(
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@ -390,7 +378,7 @@ struct llm_graph_params {
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const llama_memory_state_i * mstate;
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const llama_cross * cross;
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int32_t n_outputs;
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uint32_t n_outputs;
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const llm_graph_cb & cb;
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};
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@ -424,8 +412,8 @@ struct llm_graph_context {
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const float norm_eps;
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const float norm_rms_eps;
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const int32_t n_tokens;
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const int32_t n_outputs;
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const int64_t n_tokens;
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const int64_t n_outputs;
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const int32_t n_ctx_orig; // yarn
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const enum llama_pooling_type pooling_type;
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@ -521,7 +509,6 @@ struct llm_graph_context {
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ggml_tensor * build_inp_mean() const;
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ggml_tensor * build_inp_cls() const;
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ggml_tensor * build_inp_s_copy() const;
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ggml_tensor * build_inp_s_mask() const;
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ggml_tensor * build_inp_cross_embd() const;
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ggml_tensor * build_inp_pos_bucket_enc() const;
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@ -606,18 +593,17 @@ struct llm_graph_context {
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// recurrent
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//
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ggml_tensor * build_copy_mask_state(
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ggml_tensor * build_recurrent_state(
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ggml_cgraph * gf,
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ggml_tensor * s,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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int32_t n_state,
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int32_t n_seqs) const;
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int32_t state_size,
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int32_t n_seqs,
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bool avoid_copies = false) const;
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ggml_tensor * build_rwkv_token_shift_load(
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ggml_cgraph * gf,
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ggml_tensor * state_copy,
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ggml_tensor * state_mask,
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const llama_ubatch & ubatch,
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int il) const;
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