mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-09-25 07:27:53 -05:00
297 lines
14 KiB
C++
297 lines
14 KiB
C++
#include <algorithm>
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#include <map>
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#include <utility>
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#include "core/ggml_extend.hpp"
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#include "core/segment_graph_bindings.h"
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#include "core/segment_weight_pipeline.h"
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using namespace sd;
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static size_t add_bytes(size_t a, size_t b) {
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return b > SIZE_MAX - a ? SIZE_MAX : a + b;
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}
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ComputeWorkspace::Measurement GGMLRunner::measure(ggml_cgraph* graph, size_t direct_bytes) {
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auto external_backend = [&](const ggml_tensor* tensor) -> ggml_backend_t {
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if (!params_tensor_set_.count(tensor)) {
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return nullptr;
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}
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auto placement = graph_cut_layer_split_assignments_.find(tensor);
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return placement == graph_cut_layer_split_assignments_.end() ? runtime_backend : placement->second;
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};
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auto assign_nodes = [&](ggml_backend_sched_t scheduler, ggml_cgraph* copy) {
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pin_multi_device_nodes(scheduler, copy, graph);
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};
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return workspace_.measure(graph, direct_bytes, external_backend, assign_nodes);
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}
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std::vector<DeviceMemoryRequest> GGMLRunner::memory_requests(
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const std::vector<BackendBufferSize>& sizes,
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size_t pending_cache_bytes) const {
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std::vector<DeviceMemoryRequest> requests;
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for (const auto& size : sizes) {
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const size_t retained = retained_runtime_buffer_bytes(size.backend);
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const size_t reusable = workspace_.bytes(size.backend);
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const size_t cache_bytes = size.backend == runtime_backend ? pending_cache_bytes : 0;
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const size_t pending = add_bytes(size.bytes > reusable ? size.bytes - reusable : 0, cache_bytes);
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size_t limit = max_graph_vram_bytes;
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if (is_multi_device()) {
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size_t index = 0;
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if (size.backend != runtime_backend) {
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auto position = std::find(extra_runtime_backends.begin(), extra_runtime_backends.end(), size.backend);
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index = static_cast<size_t>(position - extra_runtime_backends.begin()) + 1;
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}
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if (index < graph_cut_layer_split_backend_vram_limits_.size()) {
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limit = graph_cut_layer_split_backend_vram_limits_[index];
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}
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}
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requests.push_back({size.backend, reinterpret_cast<uintptr_t>(this), pending,
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retained, limit});
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}
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return requests;
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}
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bool GGMLRunner::fits(const std::vector<DeviceMemoryRequest>& requests,
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const std::vector<ggml_tensor*>& params) const {
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auto manager = residency_manager.lock();
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if (manager == nullptr) {
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return params.empty();
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}
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for (const auto& request : requests) {
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if (!manager->fits_compute_backend_capacity(request, params)) {
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return false;
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}
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}
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return true;
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}
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bool GGMLRunner::execute_segment(ggml_cgraph* graph, int n_threads) {
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if (sd_backend_is_cpu(runtime_backend)) {
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sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
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}
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if (workspace_.cpu_backend() != nullptr) {
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sd_backend_cpu_set_n_threads(workspace_.cpu_backend(), n_threads);
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}
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auto scheduler = workspace_.scheduler();
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ggml_status status;
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if (scheduler != nullptr) {
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if (sd_get_backend_eval_callback() != nullptr && !multi_device_eval_callback_warned) {
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LOG_WARN("%s: eval callback is not supported with the backend scheduler; ignoring", get_desc().c_str());
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multi_device_eval_callback_warned = true;
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}
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status = ggml_backend_sched_graph_compute(scheduler, graph);
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} else {
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status = sd_backend_graph_compute_with_eval_callback(runtime_backend, graph,
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sd_get_backend_eval_callback(),
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sd_get_backend_eval_callback_data());
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}
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workspace_.synchronize();
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if (status != GGML_STATUS_SUCCESS) {
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LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
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return false;
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}
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const std::string description = get_desc();
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if (!debug_tensors.empty()) {
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std::unordered_set<const ggml_tensor*> graph_tensors;
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const int leaf_count = ggml_graph_cut::leaf_count(graph);
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const int node_count = ggml_graph_n_nodes(graph);
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graph_tensors.reserve(static_cast<size_t>(leaf_count + node_count));
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for (int index = 0; index < leaf_count; ++index) {
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graph_tensors.insert(ggml_graph_cut::leaf_tensor(graph, index));
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}
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for (int index = 0; index < node_count; ++index) {
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graph_tensors.insert(ggml_graph_node(graph, index));
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}
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for (const auto& entry : debug_tensors) {
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ggml_tensor* tensor = entry.first;
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if (tensor == nullptr || graph_tensors.find(tensor) == graph_tensors.end()) {
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continue;
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}
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ggml_backend_buffer_t buffer =
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tensor->view_src != nullptr ? tensor->view_src->buffer : tensor->buffer;
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if (buffer == nullptr) {
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LOG_WARN("%s skip debug tensor '%s': tensor buffer not set",
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description.c_str(),
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entry.second.c_str());
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continue;
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}
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if (tensor->type != GGML_TYPE_F32) {
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LOG_WARN("%s skip debug tensor '%s': only GGML_TYPE_F32 is supported, got %s",
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description.c_str(),
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entry.second.c_str(),
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ggml_type_name(tensor->type));
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continue;
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}
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auto debug_tensor = make_sd_tensor_from_ggml<float>(tensor);
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print_sd_tensor(debug_tensor, false, entry.second.c_str());
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}
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}
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return true;
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}
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std::optional<Tensor<float>> GGMLRunner::execute_graph(ggml_cgraph* graph, int n_threads, bool no_return, const std::function<bool()>& read_outputs) {
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if (!assign_graph_cut_layer_split_backends(graph)) {
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return std::nullopt;
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}
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const auto params = collect_used_param_tensors(graph);
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ggml_graph_cut::Plan plan;
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if (!resolve_graph_cut_plan(graph, &plan)) {
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return std::nullopt;
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}
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const auto full_measurement = measure(graph, plan.compute_buffer_size);
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if (full_measurement.buffers.empty()) {
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return std::nullopt;
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}
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auto manager = residency_manager.lock();
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const bool segmented = !is_multi_device() && !sd_backend_is_cpu(runtime_backend) &&
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manager != nullptr && manager->segmented_compute_enabled() &&
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plan.valid && plan.has_cuts && plan.segments.size() > 1 &&
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!fits(memory_requests(full_measurement.buffers, cache_.pending_bytes(graph)), params);
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if (!segmented) {
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ggml_graph_cut::Segment segment;
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segment.group_name = "graph";
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segment.compute_buffer_size = plan.compute_buffer_size;
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for (int i = 0; i < ggml_graph_n_nodes(graph); ++i) {
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segment.internal_node_indices.push_back(i);
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}
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for (int i = 0; i < ggml_graph_cut::leaf_count(graph); ++i) {
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auto tensor = ggml_graph_cut::leaf_tensor(graph, i);
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ggml_graph_cut::Segment::InputRef input;
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input.leaf_index = i;
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input.type = canonical_param_tensor(tensor) != nullptr
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? ggml_graph_cut::Segment::INPUT_PARAM
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: ggml_graph_cut::Segment::INPUT_EXTERNAL;
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segment.input_refs.push_back(input);
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}
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plan.segments = {std::move(segment)};
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}
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const bool segments_changed = plan.segments.size() != logged_segment_count_;
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if (segments_changed && (segmented || logged_segment_count_ > 1)) {
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LOG_DEBUG("%s using %zu segment%s", get_desc().c_str(),
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plan.segments.size(), plan.segments.size() == 1 ? "" : "s");
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}
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SegmentGraphBindings bindings(cut_cache_, plan, graph);
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SegmentWeightPipeline weights(manager, runtime_backend, reinterpret_cast<uintptr_t>(this),
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graph, plan, params_tensor_set_,
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segmented && manager != nullptr && manager->prefetch_enabled());
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std::map<ggml_backend_t, size_t> peak_compute_bytes;
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auto track_compute_buffer = [&](ggml_backend_t backend) {
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if (backend != nullptr) {
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auto& peak = peak_compute_bytes[backend];
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peak = std::max(peak, workspace_.bytes(backend));
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}
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};
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std::optional<Tensor<float>> output = Tensor<float>();
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for (size_t index = 0; index < plan.segments.size(); ++index) {
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const auto& segment = plan.segments[index];
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const bool last = index + 1 == plan.segments.size();
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auto fail_segment = [&](const char* phase) {
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LOG_ERROR("%s segment %zu/%zu (%s) failed during %s", get_desc().c_str(),
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index + 1, plan.segments.size(), segment.group_name.c_str(), phase);
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return std::nullopt;
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};
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cut_cache_.prune(segment.live_cut_names);
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bindings.reset(segment);
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if (!bindings.bind_cached_inputs(segment, get_desc().c_str())) {
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return fail_segment("input binding");
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}
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ggml_context* segment_context = nullptr;
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auto segment_graph = segmented
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? ggml_graph_cut::build_segment_graph(graph, segment, &segment_context)
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: graph;
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struct SegmentCleanup {
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GGMLRunner& runner;
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SegmentWeightPipeline& weights;
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SegmentGraphBindings& bindings;
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ggml_context* context;
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~SegmentCleanup() {
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runner.workspace_.segment_end();
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bindings.restore();
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weights.segment_end();
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ggml_free(context);
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runner.sync_runtime_residency();
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}
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} segment_cleanup{*this, weights, bindings, segment_context};
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auto measurement = segmented ? measure(segment_graph, segment.compute_buffer_size) : full_measurement;
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if (!workspace_.prepare(measurement)) {
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return fail_segment("workspace preparation");
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}
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const size_t cut_bytes = last ? 0 : cut_cache_.estimate_output_bytes(graph, segment);
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const size_t new_cache_bytes = add_bytes(cut_bytes, cache_.pending_bytes(segment_graph));
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auto ensure_capacity = [&]() {
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sync_runtime_residency();
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auto requests = memory_requests(measurement.buffers, new_cache_bytes);
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if (!fits(requests, weights.params(index)) && workspace_.release_excess(measurement)) {
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sync_runtime_residency();
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requests = memory_requests(measurement.buffers, new_cache_bytes);
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}
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return weights.ensure_segment_capacity(index, requests);
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};
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if (!weights.segment_start(index, ensure_capacity)) {
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return fail_segment("weight preparation");
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}
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// Preparing weights can execute LoRA graphs and reclaim an idle workspace.
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if (!workspace_.measurement_matches(segment_graph, measurement)) {
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measurement = measure(segment_graph, segment.compute_buffer_size);
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}
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if (!workspace_.prepare(measurement) || !ensure_capacity()) {
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return fail_segment("workspace capacity check");
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}
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if (!workspace_.allocate(segment_graph, [&](ggml_backend_sched_t scheduler, ggml_cgraph* current) {
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pin_multi_device_nodes(scheduler, current);
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})) {
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return fail_segment("workspace allocation");
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}
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for (const auto& size : measurement.buffers) {
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track_compute_buffer(size.backend);
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}
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if (workspace_.scheduler() != nullptr) {
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track_compute_buffer(workspace_.cpu_backend());
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}
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if (!ensure_capacity()) {
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return fail_segment("allocated capacity check");
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}
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copy_data_to_backend_tensor(segment_graph, false);
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auto prefetch_requests = memory_requests(measurement.buffers, new_cache_bytes);
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if (!prefetch_requests.empty()) {
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weights.enqueue_next(index, prefetch_requests.front());
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}
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if (!execute_segment(segment_graph, n_threads) ||
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!cache_.capture(segment_graph) ||
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!cut_cache_.capture(graph, segment, get_desc().c_str())) {
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return fail_segment("execution or output caching");
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}
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sync_runtime_residency();
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if (last) {
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if (read_outputs && !read_outputs()) {
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return fail_segment("output finalization");
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}
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if (!no_return) {
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auto result = ggml_get_tensor(compute_ctx, final_result_name.c_str());
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output = read_graph_tensor<float>(result, "output");
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if (!output.has_value()) {
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return fail_segment("output readback");
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}
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}
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}
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// Final outputs and their callbacks may still be views of consumed cuts.
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cut_cache_.prune(segment.future_cut_names);
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}
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if (segments_changed || peak_compute_bytes != logged_compute_bytes_) {
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for (const auto& entry : peak_compute_bytes) {
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LOG_DEBUG("%s compute buffer size: %.2f MB(%s) on %s (peak across %zu segment%s)",
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get_desc().c_str(), entry.second / (1024.0 * 1024.0),
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sd_backend_is_cpu(entry.first) ? "RAM" : "VRAM", ggml_backend_name(entry.first),
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plan.segments.size(), plan.segments.size() == 1 ? "" : "s");
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}
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logged_compute_bytes_ = std::move(peak_compute_bytes);
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logged_segment_count_ = plan.segments.size();
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}
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return output;
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}
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