From 9045c9afe57513eef8d16e329042ea2d7afd9a95 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 27 Dec 2025 09:56:04 +0100 Subject: [PATCH] llama-fit-params: fix Gemma 3 calculation (#18372) --- src/llama.cpp | 45 +++++++++++++++++++++++++-------------------- 1 file changed, 25 insertions(+), 20 deletions(-) diff --git a/src/llama.cpp b/src/llama.cpp index 1e18637e36..3428b1bd3f 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -181,12 +181,11 @@ static void llama_params_fit_impl( } } - int64_t sum_total = 0; + int64_t sum_free = 0; int64_t sum_projected_free = 0; int64_t min_projected_free = INT64_MAX; int64_t sum_projected_used = 0; int64_t sum_projected_model = 0; - int64_t sum_projected_ctx = 0; if (nd > 1) { LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__); @@ -197,12 +196,11 @@ static void llama_params_fit_impl( const int64_t projected_used = dmd.mb.total(); const int64_t projected_free = dmd.free - projected_used; - sum_total += dmd.total; + sum_free += dmd.free; sum_projected_used += projected_used; sum_projected_free += projected_free; min_projected_free = std::min(min_projected_free, projected_free); sum_projected_model += dmd.mb.model; - sum_projected_ctx += dmd.mb.context; if (nd > 1) { LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n", @@ -210,10 +208,9 @@ static void llama_params_fit_impl( projected_free >= 0 ? "surplus" : "deficit"); } } - assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0); - assert(sum_projected_used >= sum_projected_ctx); + assert(sum_free >= 0 && sum_projected_used >= 0); LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n", - __func__, sum_projected_used/MiB, sum_total/MiB); + __func__, sum_projected_used/MiB, sum_free/MiB); if (min_projected_free >= margin) { if (nd == 1) { LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n", @@ -236,9 +233,7 @@ static void llama_params_fit_impl( __func__, margin/MiB, -global_surplus/MiB); if (cparams->n_ctx == 0) { if (hp_nct > n_ctx_min) { - const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct; - - int64_t memory_reduction = -global_surplus; + int64_t sum_used_target = sum_free - nd*margin_s; if (nd > 1) { // for multiple devices we need to be more conservative in terms of how much context we think can fit: // - for dense models only whole layers can be assigned to devices @@ -246,24 +241,34 @@ static void llama_params_fit_impl( // - on average we expect a waste of 0.5 layers/tensors per device // - use slightly more than the expected average for nd devices to be safe const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl); - memory_reduction += (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6); + sum_used_target -= (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6); } - uint32_t ctx_reduction = std::min(uint32_t((memory_reduction + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min); - cparams->n_ctx = hp_nct - ctx_reduction; - cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend + int64_t sum_projected_used_min_ctx = 0; + cparams->n_ctx = n_ctx_min; + const dmds_t dmds_min_ctx = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + for (const auto & dmd : dmds_min_ctx) { + sum_projected_used_min_ctx += dmd.mb.total(); + } + if (sum_used_target > sum_projected_used_min_ctx) { + // linear interpolation between minimum and maximum context size: + cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx) + / (sum_projected_used - sum_projected_used_min_ctx); + cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend - ctx_reduction = hp_nct - cparams->n_ctx; - memory_reduction = ctx_reduction * bytes_per_ctx; - global_surplus += memory_reduction; - LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n", - __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); - if (global_surplus >= 0) { + const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min); + const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx; + LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n", + __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); if (nd == 1) { LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__); return; } LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__); + } else { + const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx; + LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n", + __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); } } else { LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",