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https://github.com/ggml-org/llama.cpp.git
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graph : do not include llama-model.h
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+4
-7
@@ -3,7 +3,6 @@
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#include "llama-impl.h"
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#include "llama-batch.h"
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#include "llama-cparams.h"
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#include "llama-model.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-iswa.h"
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@@ -610,6 +609,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
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rope_type (hparams.rope_type),
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sched (params.sched),
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backend_cpu (params.backend_cpu),
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dev_out (params.dev_out),
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cvec (params.cvec),
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loras (params.loras),
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mctx (params.mctx),
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@@ -2049,8 +2049,7 @@ void llm_graph_context::build_pooling(
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ggml_build_forward_expand(gf, cur);
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}
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void llm_graph_context::build_sampling(const llama_model & model, const llm_graph_params & params) const {
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GGML_UNUSED(params);
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void llm_graph_context::build_sampling() const {
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if (samplers.empty()) {
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return;
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}
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@@ -2074,11 +2073,9 @@ void llm_graph_context::build_sampling(const llama_model & model, const llm_grap
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ggml_tensor * logits_t = res->t_logits;
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GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
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const int n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(&model));
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GGML_ASSERT(logits_t->ne[0] == n_vocab);
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const int64_t n_vocab = logits_t->ne[0];
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ggml_backend_dev_t device = model.dev_output();
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ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(device);
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ggml_backend_buffer_type_t buft = ggml_backend_dev_buffer_type(dev_out);
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std::unordered_map<llama_seq_id, llama_sampler*> active_samplers;
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