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Merge branch 'master' into HEAD
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@@ -1,5 +1,6 @@
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#include "llama-context.h"
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#include "llama-arch.h"
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-io.h"
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@@ -322,7 +323,7 @@ llama_context::llama_context(
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cross.v_embd.clear();
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const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
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const uint32_t n_seqs = cparams.n_seq_max;
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const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
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// avoid reserving graphs with zero outputs - assume one output per sequence
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@@ -575,7 +576,7 @@ bool llama_context::memory_update(bool optimize) {
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throw std::runtime_error("failed to initialize memory context");
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}
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const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
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const uint32_t n_seqs = cparams.n_seq_max;
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const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
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auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
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@@ -1849,6 +1850,9 @@ void llama_context::output_reorder() {
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//
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uint32_t llama_context::graph_max_nodes() const {
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if (model.arch == LLM_ARCH_QWEN3NEXT) {
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return std::max<uint32_t>(8192u, 32u*model.n_tensors());
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}
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return std::max<uint32_t>(1024u, 8u*model.n_tensors());
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}
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