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@@ -1812,6 +1812,12 @@ static bool llama_eval_internal(
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// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
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n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
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+ struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
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+ struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
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+
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+ LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
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+ LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
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+
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#if GGML_USE_MPI
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const int64_t n_layer = hparams.n_layer;
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ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
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@@ -1825,7 +1831,10 @@ static bool llama_eval_internal(
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//}
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ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
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ggml_metal_graph_compute(lctx.ctx_metal, gf);
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- ggml_metal_get_tensor (lctx.ctx_metal, cur);
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+ ggml_metal_get_tensor (lctx.ctx_metal, res);
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+ if (!lctx.embedding.empty()) {
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+ ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
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+ }
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} else {
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// IMPORTANT:
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// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
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@@ -1856,12 +1865,6 @@ static bool llama_eval_internal(
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// update kv token count
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lctx.kv_self.n = n_past + N;
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- struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
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- struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
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-
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- LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
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- LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
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-
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if (cgraph_fname) {
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ggml_graph_export(gf, cgraph_fname);
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}
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