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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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@ -107,7 +107,7 @@ int main(int argc, char ** argv) {
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const llama_vocab * vocab = llama_model_get_vocab(model);
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const int n_ctx_train = llama_model_n_ctx_train(model);
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const int n_ctx = llama_n_ctx(ctx);
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const int n_ctx = llama_n_ctx(ctx);
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const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
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@ -224,6 +224,7 @@ int main(int argc, char ** argv) {
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auto & client = clients[i];
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client.id = i;
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client.smpl = common_sampler_init(model, params.sampling);
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//params.sampling.seed++;
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}
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std::vector<llama_token> tokens_system;
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@ -345,7 +346,7 @@ int main(int argc, char ** argv) {
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client.n_decoded = 0;
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client.i_batch = batch.n_tokens - 1;
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LOG_INF("\033[31mClient %3d, seq %4d, junk = %4d, started decoding ...\033[0m\n", client.id, client.seq_id, n_junk_cur);
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LOG_INF("\033[31mClient %3d, seq %4d, junk = %4d, prompt = %d, started decoding ...\033[0m\n", client.id, client.seq_id, n_junk_cur, client.n_prompt);
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g_seq_id += 1;
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