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llama : add high-throughput mode (#14363)
* kv-cache : prepare K/V buffers for separation ggml-ci * batched-bench : fix oob write ggml-ci * llama : add "virtual sequences" ggml-ci * llama : use "stream" vs "virtual sequence" ggml-ci * graph : fix stream splitting when KV cache is not used ggml-ci * kv-cache : add multi-stream save/load support ggml-ci * llama : add "--attn-streams" flag ggml-ci * kv-cache : fix handling when find_slot fails ggml-ci * kv-cache : restore find_slot impl ggml-ci * kv-cache : add comments * kv-cache : add bounds checks for sequence id ggml-ci * cont : add n_seq_max to batch allocr ggml-ci * kv-cache : perform stream copies lazily after llama_synchronize ggml-ci * kv-cache : avoid throwing exceptions across the C boundary ggml-ci * CUDA: 4D FlashAttention support (#14628) * CUDA: 4D FlashAttention support * CUDA: fix WMMA FA kernel * llama : rename attn_streams -> kv_unified ggml-ci * common : rename kv_split -> kv_unified ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
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@@ -191,6 +191,14 @@ struct llama_hparams {
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// dimension of value embeddings across all k-v heads
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uint32_t n_embd_v_gqa(uint32_t il = 0) const;
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// true if any layer has a different n_embd_k_gqa/n_embd_v_gqa
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bool is_n_embd_k_gqa_variable() const;
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bool is_n_embd_v_gqa_variable() const;
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// return the maximum n_embd_k_gqa/n_embd_v_gqa across all layers
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uint32_t n_embd_k_gqa_max() const;
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uint32_t n_embd_v_gqa_max() const;
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// dimension of the rolling state embeddings
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// corresponds to Mamba's conv_states size or RWKV's token_shift states size
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uint32_t n_embd_r() const;
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