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chore: format code
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@@ -568,7 +568,7 @@ bool ModelManager::stage_tensors_to_compute_backend(const std::vector<TensorStat
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ggml_backend_alloc_ctx_tensors_from_buft(staging_ctx, staging_buft);
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if (compute_buffer == nullptr) {
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LOG_ERROR("model manager alloc compute params backend buffer failed, num_tensors = %zu",
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staged_tensors.size());
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staged_tensors.size());
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ggml_free(staging_ctx);
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return false;
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}
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@@ -692,8 +692,8 @@ bool ModelManager::apply_loras_to_params(const std::vector<TensorState*>& states
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// The temporary runner is destroyed before this manager call returns.
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auto borrowed_manager = std::shared_ptr<ModelManager>(this, [](ModelManager*) {});
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auto lora = std::make_shared<LoraModel>(id, compute_backend, target->params_backend,
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borrowed_manager, lora_spec.file_id, lora_version_,
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target->residency_mode);
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borrowed_manager, lora_spec.file_id, lora_version_,
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target->residency_mode);
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LoraModel::filter_t lora_tensor_filter = nullptr;
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if (!lora_spec.tensor_name_prefix_filter.empty()) {
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@@ -1409,8 +1409,8 @@ bool ModelManager::assign_compute_backend(const std::vector<ggml_tensor*>& tenso
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const bool params_follow_compute = state->params_follow_compute_backend ||
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state->residency_mode == ResidencyMode::Disk;
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const bool compute_changes = state->compute_backend != compute_backend;
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const bool params_changes = params_follow_compute && state->params_backend != compute_backend;
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const bool compute_changes = state->compute_backend != compute_backend;
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const bool params_changes = params_follow_compute && state->params_backend != compute_backend;
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if (!compute_changes && !params_changes) {
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continue;
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}
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@@ -1501,7 +1501,7 @@ size_t ModelManager::compute_backend_resident_bytes(ggml_backend_t compute_backe
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? buffer_type != nullptr && ggml_backend_buft_get_device(buffer_type) == compute_device
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: std::any_of(split_devices->second.begin(), split_devices->second.end(), [&](const auto& entry) {
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return ggml_backend_get_device(entry.first) == compute_device;
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});
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});
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if (!on_device) {
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return;
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}
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@@ -1782,10 +1782,10 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
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const size_t resident = add(weights_resident, add(other_runtime, request.runtime_resident_bytes));
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if (log_details) {
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LOG_WARN("model manager memory on %s: reported free %.2f MB / total %.2f MB, tracked weights %.2f MB / other runtime %.2f MB / current runtime %.2f MB",
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ggml_backend_name(backend),
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free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0),
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weights_resident / (1024.0 * 1024.0), other_runtime / (1024.0 * 1024.0),
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request.runtime_resident_bytes / (1024.0 * 1024.0));
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ggml_backend_name(backend),
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free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0),
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weights_resident / (1024.0 * 1024.0), other_runtime / (1024.0 * 1024.0),
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request.runtime_resident_bytes / (1024.0 * 1024.0));
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}
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if (free_bytes == 0 && total_bytes == 0) {
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return SIZE_MAX;
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@@ -1797,7 +1797,7 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
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}
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// A wrapped report may reflect real pressure; reclaim before accepting the tracked estimate.
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LOG_VERBOSE("ignoring impossible free memory report on %s (%.2f MB free / %.2f MB total), using tracked residency",
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ggml_backend_name(backend), free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0));
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ggml_backend_name(backend), free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0));
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free_bytes = total_bytes;
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result.requires_reclamation = true;
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}
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@@ -1907,9 +1907,9 @@ bool ModelManager::ensure_compute_backend_capacity(
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}
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const bool reloadable = state->residency_mode == ResidencyMode::Disk ||
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state->compute_backend != state->params_backend;
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const bool resident = state->compute_backend == state->params_backend
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? state->loaded_to_params_backend
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: state->staged_to_compute_backend;
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const bool resident = state->compute_backend == state->params_backend
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? state->loaded_to_params_backend
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: state->staged_to_compute_backend;
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if (reloadable && resident) {
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eviction_states.insert(state);
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}
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