mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-09-24 20:20:37 +00:00
1004 lines
40 KiB
C++
1004 lines
40 KiB
C++
#include <algorithm>
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#include <exception>
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#include <map>
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#include <utility>
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#include "core/ggml_extend.h"
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#include "core/ggml_extend_backend.h"
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#include "core/ggml_runner.h"
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#include "core/ggml_tensor_utils.h"
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#include "core/layer_split_partition.h"
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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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ggml_tensor* ggml_ext_attention_ext(GGMLRunnerContext* ctx,
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ggml_tensor* q,
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ggml_tensor* k,
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ggml_tensor* v,
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int64_t n_head,
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ggml_tensor* mask,
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bool skip_reshape,
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bool flash_attn,
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float kv_scale) {
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if (ctx->attn_scale > 0.f) {
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kv_scale = ctx->attn_scale;
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}
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return ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, n_head, mask, skip_reshape, flash_attn, kv_scale, ctx->sage_attn_enabled);
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}
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void GGMLRunner::alloc_params_ctx() {
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ggml_init_params params;
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params.mem_size = static_cast<size_t>(MAX_PARAMS_TENSOR_NUM * ggml_tensor_overhead());
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params.mem_buffer = nullptr;
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params.no_alloc = true;
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params_ctx = ggml_init(params);
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GGML_ASSERT(params_ctx != nullptr);
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params_tensor_set_.clear();
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params_tensor_set_dirty_ = true;
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}
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void GGMLRunner::free_params_ctx() {
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if (params_ctx != nullptr) {
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ggml_free(params_ctx);
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params_ctx = nullptr;
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}
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params_tensor_set_.clear();
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params_tensor_set_dirty_ = true;
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}
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void GGMLRunner::alloc_compute_ctx() {
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ggml_init_params params;
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params.mem_size = static_cast<size_t>(ggml_tensor_overhead() * MAX_GRAPH_SIZE + ggml_graph_overhead());
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params.mem_buffer = nullptr;
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params.no_alloc = true;
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compute_ctx = ggml_init(params);
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GGML_ASSERT(compute_ctx != nullptr);
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}
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void GGMLRunner::free_compute_ctx() {
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debug_tensors.clear();
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if (compute_ctx != nullptr) {
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ggml_free(compute_ctx);
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compute_ctx = nullptr;
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}
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backend_tensor_data_map.clear();
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}
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void GGMLRunner::rebuild_params_tensor_set() {
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if (!params_tensor_set_dirty_) {
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return;
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}
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params_tensor_set_.clear();
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if (params_ctx == nullptr) {
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return;
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}
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for (ggml_tensor* t = ggml_get_first_tensor(params_ctx); t != nullptr; t = ggml_get_next_tensor(params_ctx, t)) {
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params_tensor_set_.insert(t);
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}
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params_tensor_set_dirty_ = false;
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}
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ggml_tensor* GGMLRunner::canonical_param_tensor(ggml_tensor* tensor) {
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for (auto* current = tensor; current != nullptr; current = current->view_src) {
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if (params_tensor_set_.count(current) != 0)
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return current;
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}
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return nullptr;
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}
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std::vector<ggml_tensor*> GGMLRunner::collect_used_param_tensors(ggml_cgraph* gf) {
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std::vector<ggml_tensor*> used_params;
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rebuild_params_tensor_set();
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if (gf == nullptr || params_tensor_set_.empty()) {
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return used_params;
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}
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std::unordered_set<const ggml_tensor*> seen_params;
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const int n_leafs = sd::ggml_graph_cut::leaf_count(gf);
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seen_params.reserve(static_cast<size_t>(n_leafs));
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for (int i = 0; i < n_leafs; ++i) {
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ggml_tensor* leaf = sd::ggml_graph_cut::leaf_tensor(gf, i);
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ggml_tensor* param_leaf = canonical_param_tensor(leaf);
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if (param_leaf != nullptr &&
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seen_params.insert(param_leaf).second) {
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used_params.push_back(param_leaf);
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}
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}
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return used_params;
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}
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void GGMLRunner::evict_compute_backend_param_tensors(const std::vector<ggml_tensor*>& tensors) {
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if (tensors.empty()) {
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return;
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}
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auto manager = residency_manager.lock();
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if (manager != nullptr) {
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manager->evict_compute_backend_params(tensors);
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}
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}
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void GGMLRunner::prepare_build_in_tensor_before() {
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one_tensor = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_F32, 1);
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ggml_set_name(one_tensor, "ggml_runner_build_in_tensor:one");
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set_backend_tensor_data(one_tensor, one_vec.data());
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zero_int_tensor = ggml_new_tensor_1d(compute_ctx, GGML_TYPE_I32, 1);
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ggml_set_name(zero_int_tensor, "ggml_runner_build_in_tensor:zero_int");
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set_backend_tensor_data(zero_int_tensor, zero_int_vec.data());
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}
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void GGMLRunner::prepare_build_in_tensor_after(ggml_cgraph* gf) {
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ggml_build_forward_expand(gf, one_tensor);
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ggml_build_forward_expand(gf, zero_int_tensor);
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}
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ggml_cgraph* GGMLRunner::new_graph_custom(size_t graph_size) {
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if (weight_adapter) {
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graph_size += weight_adapter->get_extra_graph_size();
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}
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return ggml_new_graph_custom(compute_ctx, graph_size, false);
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}
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ggml_cgraph* GGMLRunner::get_compute_graph(get_graph_cb_t get_graph) {
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prepare_build_in_tensor_before();
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ggml_cgraph* gf = get_graph();
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if (gf == nullptr) {
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return nullptr;
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}
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if (ggml_graph_n_nodes(gf) > 0) {
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auto result = ggml_graph_node(gf, -1);
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ggml_set_name(result, final_result_name.c_str());
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}
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for (const auto& entry : debug_tensors) {
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if (entry.first != nullptr) {
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ggml_build_forward_expand(gf, entry.first);
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}
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}
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for (const auto& entry : cache_.outputs()) {
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if (entry.second != nullptr) {
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ggml_build_forward_expand(gf, entry.second);
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}
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}
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prepare_build_in_tensor_after(gf);
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return gf;
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}
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bool GGMLRunner::prepare_compute_graph(get_graph_cb_t get_graph,
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ggml_cgraph** gf_out) {
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GGML_ASSERT(gf_out != nullptr);
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reset_compute_ctx();
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ggml_cgraph* gf = get_compute_graph(get_graph);
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if (gf == nullptr) {
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free_compute_ctx();
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return false;
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}
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*gf_out = gf;
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return true;
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}
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ggml_backend_t GGMLRunner::backend_for_weight(const ggml_tensor* tensor) const {
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if (tensor == nullptr || tensor->buffer == nullptr) {
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return nullptr;
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}
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if (ggml_backend_buffer_get_usage(tensor->buffer) != GGML_BACKEND_BUFFER_USAGE_WEIGHTS ||
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ggml_backend_buffer_is_host(tensor->buffer)) {
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return nullptr;
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}
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ggml_backend_dev_t dev = ggml_backend_buft_get_device(ggml_backend_buffer_get_type(tensor->buffer));
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if (dev == nullptr) {
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return nullptr;
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}
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if (ggml_backend_get_device(runtime_backend) == dev) {
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return runtime_backend;
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}
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for (ggml_backend_t backend : extra_runtime_backends) {
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if (ggml_backend_get_device(backend) == dev) {
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return backend;
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}
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}
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return nullptr;
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}
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void GGMLRunner::pin_multi_device_nodes(ggml_backend_sched_t sched, ggml_cgraph* gf, ggml_cgraph* original_graph) {
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if (sched == nullptr || gf == nullptr) {
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return;
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}
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ggml_backend_t current = runtime_backend;
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const int n_nodes = ggml_graph_n_nodes(gf);
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for (int i = 0; i < n_nodes; i++) {
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ggml_tensor* node = ggml_graph_node(gf, i);
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auto node_assignment = graph_cut_layer_split_node_assignments_.find(original_graph == nullptr ? node : ggml_graph_node(original_graph, i));
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if (node_assignment != graph_cut_layer_split_node_assignments_.end()) {
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current = node_assignment->second;
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}
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for (int s = 0; s < GGML_MAX_SRC; s++) {
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ggml_backend_t weight_backend = backend_for_weight(node->src[s]);
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if (weight_backend != nullptr) {
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if (node_assignment == graph_cut_layer_split_node_assignments_.end()) {
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current = weight_backend;
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}
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}
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}
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if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE ||
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node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) {
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continue;
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}
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if (ggml_backend_supports_op(current, node)) {
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ggml_backend_sched_set_tensor_backend(sched, node, current);
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}
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}
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}
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size_t GGMLRunner::retained_runtime_buffer_bytes(ggml_backend_t backend) const {
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backend = backend == nullptr ? runtime_backend : backend;
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size_t bytes = workspace_.bytes(backend);
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if (backend == runtime_backend) {
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const size_t cache_bytes = cache_.resident_bytes(ggml_backend_get_device(backend));
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bytes = cache_bytes > SIZE_MAX - bytes ? SIZE_MAX : bytes + cache_bytes;
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const size_t cut_bytes = cut_cache_.resident_bytes(ggml_backend_get_device(backend));
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bytes = cut_bytes > SIZE_MAX - bytes ? SIZE_MAX : bytes + cut_bytes;
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}
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return bytes;
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}
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void GGMLRunner::sync_runtime_residency() {
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if (auto manager = residency_manager.lock()) {
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manager->update_runtime_residency(reinterpret_cast<uintptr_t>(this),
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runtime_backend, retained_runtime_buffer_bytes());
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for (auto backend : extra_runtime_backends) {
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manager->update_runtime_residency(reinterpret_cast<uintptr_t>(this),
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backend, retained_runtime_buffer_bytes(backend));
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}
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}
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}
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std::optional<sd::Tensor<float>> GGMLRunner::read_graph_tensor(ggml_tensor* tensor, const char* label) {
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if (tensor == nullptr) {
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LOG_ERROR("%s %s tensor is null", get_desc().c_str(), label);
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return std::nullopt;
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}
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if (tensor->type != GGML_TYPE_F32) {
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LOG_ERROR("%s %s tensor type mismatch: got %s",
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get_desc().c_str(),
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label,
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ggml_type_name(tensor->type));
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return std::nullopt;
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}
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ggml_backend_buffer_t buf = sd::ggml_graph_cut::tensor_buffer(tensor);
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if (buf == nullptr) {
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LOG_ERROR("%s %s tensor buffer missing: name=%s op=%s buffer=%p view_src=%p view_src_buffer=%p data=%p",
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get_desc().c_str(),
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label,
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tensor->name[0] != '\0' ? tensor->name : "<unnamed>",
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ggml_op_name(tensor->op),
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tensor->buffer,
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tensor->view_src,
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tensor->view_src ? tensor->view_src->buffer : nullptr,
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tensor->data);
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return std::nullopt;
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}
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return sd::make_sd_tensor_from_ggml<float>(tensor);
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}
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void GGMLRunner::copy_data_to_backend_tensor(ggml_cgraph* gf, bool clear_after_copy) {
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GGML_ASSERT(gf != nullptr);
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std::unordered_set<const ggml_tensor*> graph_tensor_set;
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const int n_leafs = sd::ggml_graph_cut::leaf_count(gf);
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const int n_nodes = ggml_graph_n_nodes(gf);
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graph_tensor_set.reserve(static_cast<size_t>(n_leafs + n_nodes));
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for (int i = 0; i < n_leafs; ++i) {
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graph_tensor_set.insert(sd::ggml_graph_cut::leaf_tensor(gf, i));
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}
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for (int i = 0; i < n_nodes; ++i) {
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graph_tensor_set.insert(ggml_graph_node(gf, i));
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}
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for (auto& kv : backend_tensor_data_map) {
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auto tensor = kv.first;
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auto data = kv.second;
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if (tensor == nullptr || data == nullptr) {
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continue;
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}
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const char* name = ggml_get_name(tensor);
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if (graph_tensor_set.find(tensor) == graph_tensor_set.end()) {
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continue;
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}
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if (tensor->buffer == nullptr) {
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LOG_WARN("%s skip backend tensor copy: tensor buffer not set, name='%s', ne=[%lld,%lld,%lld,%lld], type=%s",
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get_desc().c_str(),
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name != nullptr ? name : "",
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(long long)tensor->ne[0],
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(long long)tensor->ne[1],
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(long long)tensor->ne[2],
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(long long)tensor->ne[3],
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ggml_type_name(tensor->type));
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continue;
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}
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ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
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if (buf == nullptr) {
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LOG_WARN("%s graph exec skip tensor copy: name=%s op=%s reason=buffer_not_set data=%p view_src=%p view_src_buffer=%p",
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get_desc().c_str(),
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tensor && tensor->name[0] != '\0' ? tensor->name : "<unnamed>",
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tensor ? ggml_op_name(tensor->op) : "<null>",
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data,
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tensor ? tensor->view_src : nullptr,
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(tensor && tensor->view_src) ? tensor->view_src->buffer : nullptr);
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continue;
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}
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ggml_backend_tensor_set(tensor, data, 0, ggml_nbytes(tensor));
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}
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if (clear_after_copy) {
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backend_tensor_data_map.clear();
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}
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}
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const GGMLRunner::GraphCutPlan& GGMLRunner::resolve_graph_cut_plan(ggml_cgraph* gf) {
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GGML_ASSERT(gf != nullptr);
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return sd::ggml_graph_cut::resolve_plan(runtime_backend,
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gf,
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&graph_cut_plan_cache_,
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params_tensor_set_,
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get_desc().c_str());
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}
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const GGMLRunner::GraphCutPlan& GGMLRunner::resolve_graph_cut_layer_split_plan(ggml_cgraph* gf) {
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return resolve_graph_cut_plan(gf);
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}
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bool GGMLRunner::assign_graph_cut_layer_split_backends(ggml_cgraph* gf) {
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graph_cut_layer_split_node_assignments_.clear();
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if (!graph_cut_layer_split_enabled) {
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return true;
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}
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if (!is_multi_device()) {
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LOG_ERROR("%s graph-cut layer split requires multiple runtime backends", get_desc().c_str());
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return false;
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}
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const auto& plan = resolve_graph_cut_layer_split_plan(gf);
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if (!plan.valid || !plan.has_cuts || plan.segments.size() <= 1) {
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auto manager = residency_manager.lock();
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if (manager == nullptr) {
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LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
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return false;
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}
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std::vector<ggml_tensor*> graph_params = collect_used_param_tensors(gf);
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if (!graph_params.empty() &&
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!manager->assign_compute_backend(graph_params, runtime_backend)) {
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LOG_ERROR("%s graph-cut layer split failed to assign unmarked graph params to %s",
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get_desc().c_str(),
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sd::layer_split_backend_device_display_name(runtime_backend).c_str());
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return false;
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}
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for (ggml_tensor* param : graph_params) {
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if (param != nullptr) {
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graph_cut_layer_split_assignments_[param] = runtime_backend;
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}
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}
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const int n_nodes = ggml_graph_n_nodes(gf);
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for (int i = 0; i < n_nodes; i++) {
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ggml_tensor* node = ggml_graph_node(gf, i);
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if (node != nullptr) {
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graph_cut_layer_split_node_assignments_[node] = runtime_backend;
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}
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}
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if (!graph_cut_layer_split_primary_notice_logged_) {
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LOG_WARN("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
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get_desc().c_str(),
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sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
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graph_params.size());
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graph_cut_layer_split_primary_notice_logged_ = true;
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} else {
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LOG_VERBOSE("%s graph-cut layer split: graph has no mark_graph_cut segments; using primary backend %s for %zu graph params",
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get_desc().c_str(),
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sd::layer_split_backend_device_display_name(runtime_backend).c_str(),
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graph_params.size());
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}
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return true;
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}
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std::vector<ggml_backend_t> split_backends;
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split_backends.reserve(extra_runtime_backends.size() + 1);
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split_backends.push_back(runtime_backend);
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for (ggml_backend_t backend : extra_runtime_backends) {
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if (backend != nullptr) {
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split_backends.push_back(backend);
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}
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}
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auto manager = residency_manager.lock();
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if (manager == nullptr) {
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LOG_ERROR("%s weight manager is not set for graph-cut layer split", get_desc().c_str());
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return false;
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}
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sd::GraphCutLayerSplitAssignment assignment;
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auto canonicalize_param = [this](ggml_tensor* tensor) {
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return canonical_param_tensor(tensor);
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};
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if (!sd::partition_graph_cut_layer_split(get_desc().c_str(),
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gf,
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plan,
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split_backends,
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graph_cut_layer_split_backend_vram_limits_,
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max_graph_vram_bytes,
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graph_cut_layer_split_assignments_,
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canonicalize_param,
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&assignment)) {
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return false;
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}
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for (size_t i = 0; i < split_backends.size(); i++) {
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if (assignment.tensors_by_backend[i].empty()) {
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continue;
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}
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if (!manager->assign_compute_backend(assignment.tensors_by_backend[i], split_backends[i])) {
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LOG_ERROR("%s graph-cut layer split failed to assign params to %s",
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get_desc().c_str(),
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sd::layer_split_backend_device_display_name(split_backends[i]).c_str());
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return false;
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}
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}
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graph_cut_layer_split_node_assignments_ = std::move(assignment.node_assignments);
|
|
sd::log_graph_cut_layer_split_assignment(get_desc().c_str(), split_backends, assignment);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool GGMLRunner::runner_start() {
|
|
if (runner_started_) {
|
|
return true;
|
|
}
|
|
cache_.clear();
|
|
workspace_.set_extra_backends(extra_runtime_backends);
|
|
if (auto manager = residency_manager.lock()) {
|
|
manager->set_workspace_reclaimer(reinterpret_cast<uintptr_t>(this), [this]() {
|
|
if (!workspace_.release()) {
|
|
return false;
|
|
}
|
|
sync_runtime_residency();
|
|
return true;
|
|
});
|
|
}
|
|
runner_started_ = true;
|
|
return true;
|
|
}
|
|
|
|
void GGMLRunner::runner_end() {
|
|
GGML_ASSERT(!graph_active_);
|
|
if (!runner_started_) {
|
|
return;
|
|
}
|
|
workspace_.release();
|
|
cache_.clear();
|
|
logged_compute_bytes_.clear();
|
|
logged_segment_count_ = 0;
|
|
if (auto manager = residency_manager.lock()) {
|
|
manager->clear_prefetched_params(reinterpret_cast<uintptr_t>(this));
|
|
std::vector<ggml_tensor*> tensors;
|
|
for (auto tensor : params_tensor_set_) {
|
|
auto* parameter = manager->resolve_param_tensor(const_cast<ggml_tensor*>(tensor));
|
|
if (parameter != nullptr)
|
|
tensors.push_back(parameter);
|
|
}
|
|
manager->evict_compute_backend_params(tensors);
|
|
manager->remove_runtime_owner(reinterpret_cast<uintptr_t>(this));
|
|
}
|
|
runner_started_ = false;
|
|
}
|
|
|
|
GGMLRunner::GGMLRunner(ggml_backend_t backend,
|
|
std::shared_ptr<DeviceResidencyManager> manager)
|
|
: runtime_backend(backend),
|
|
cache_(backend),
|
|
cut_cache_(backend),
|
|
workspace_(backend),
|
|
residency_manager(manager) {
|
|
GGML_ASSERT(runtime_backend != nullptr);
|
|
alloc_params_ctx();
|
|
}
|
|
|
|
GGMLRunner::~GGMLRunner() {
|
|
runner_end();
|
|
free_compute_ctx();
|
|
free_params_ctx();
|
|
}
|
|
|
|
GGMLRunnerContext GGMLRunner::get_context() {
|
|
GGMLRunnerContext runner_ctx;
|
|
runner_ctx.ggml_ctx = compute_ctx;
|
|
runner_ctx.backend = runtime_backend;
|
|
runner_ctx.flash_attn_enabled = flash_attn_enabled;
|
|
runner_ctx.sage_attn_enabled = sage_attn_enabled;
|
|
runner_ctx.linear_scale = linear_scale;
|
|
runner_ctx.attn_scale = attn_scale;
|
|
runner_ctx.conv2d_direct_enabled = conv2d_direct_enabled;
|
|
runner_ctx.conv3d_direct_enabled = conv3d_direct_enabled;
|
|
runner_ctx.circular_x_enabled = circular_x_enabled;
|
|
runner_ctx.circular_y_enabled = circular_y_enabled;
|
|
runner_ctx.weight_adapter = weight_adapter;
|
|
runner_ctx.debug_tensors = &debug_tensors;
|
|
runner_ctx.get_cache_tensor = [this](const std::string& name) {
|
|
return this->get_cache_tensor_by_name(name);
|
|
};
|
|
runner_ctx.cache_tensor = [this](const std::string& name, ggml_tensor* tensor) {
|
|
this->cache(name, tensor);
|
|
};
|
|
runner_ctx.set_backend_tensor_data = [this](ggml_tensor* tensor, const void* data) {
|
|
this->set_backend_tensor_data(tensor, data);
|
|
};
|
|
return runner_ctx;
|
|
}
|
|
|
|
void GGMLRunner::reset_compute_ctx() {
|
|
free_compute_ctx();
|
|
alloc_compute_ctx();
|
|
}
|
|
|
|
void GGMLRunner::free_cache_ctx_and_buffer() {
|
|
cache_.clear();
|
|
sync_runtime_residency();
|
|
}
|
|
|
|
void GGMLRunner::set_backend_tensor_data(ggml_tensor* tensor, const void* data) {
|
|
// The scheduler only allocates standalone data tensors when they are
|
|
// marked as graph inputs. The flag is harmless for single-backend graphs.
|
|
ggml_set_input(tensor);
|
|
backend_tensor_data_map[tensor] = data;
|
|
}
|
|
|
|
ggml_tensor* GGMLRunner::to_backend(ggml_tensor* tensor) {
|
|
GGML_ASSERT(compute_ctx != nullptr);
|
|
if (tensor == nullptr) {
|
|
return nullptr;
|
|
}
|
|
// it's performing a compute, check if backend isn't cpu
|
|
if (!sd_backend_is_cpu(runtime_backend) && (tensor->buffer == nullptr || ggml_backend_buffer_is_host(tensor->buffer))) {
|
|
// pass input tensors to gpu memory
|
|
auto backend_tensor = ggml_dup_tensor(compute_ctx, tensor);
|
|
|
|
set_backend_tensor_data(backend_tensor, tensor->data);
|
|
return backend_tensor;
|
|
} else {
|
|
return tensor;
|
|
}
|
|
}
|
|
|
|
void GGMLRunner::cache(const std::string name, ggml_tensor* tensor) {
|
|
if (tensor != nullptr && tensor->view_src != nullptr) {
|
|
tensor = ggml_cont(compute_ctx, tensor);
|
|
}
|
|
if (tensor != nullptr) {
|
|
ggml_set_output(tensor);
|
|
}
|
|
cache_.stage(name, tensor);
|
|
}
|
|
|
|
std::optional<sd::Tensor<float>> GGMLRunner::compute(get_graph_cb_t get_graph,
|
|
int n_threads,
|
|
bool auto_runner_end,
|
|
bool no_return,
|
|
const std::function<bool()>& read_outputs) {
|
|
last_compute_status_ = GGML_STATUS_FAILED;
|
|
if (graph_active_) {
|
|
LOG_ERROR("%s does not support reentrant graph execution", get_desc().c_str());
|
|
return std::nullopt;
|
|
}
|
|
if (!runner_start()) {
|
|
runner_end();
|
|
return std::nullopt;
|
|
}
|
|
struct RunnerEndGuard {
|
|
GGMLRunner& runner;
|
|
bool enabled;
|
|
~RunnerEndGuard() {
|
|
if (enabled) {
|
|
runner.runner_end();
|
|
}
|
|
}
|
|
} runner_guard{*this, auto_runner_end};
|
|
graph_active_ = true;
|
|
bool success = false;
|
|
struct GraphEndGuard {
|
|
GGMLRunner& runner;
|
|
const bool& success;
|
|
~GraphEndGuard() {
|
|
if (!runner.workspace_.segment_end()) {
|
|
runner.last_compute_status_ = GGML_STATUS_FAILED;
|
|
}
|
|
runner.cache_.graph_end(false);
|
|
runner.cut_cache_.clear();
|
|
runner.free_compute_ctx();
|
|
runner.graph_active_ = false;
|
|
if (!success) {
|
|
runner.workspace_.release();
|
|
}
|
|
runner.sync_runtime_residency();
|
|
}
|
|
} graph_guard{*this, success};
|
|
|
|
ggml_cgraph* graph = nullptr;
|
|
if (!prepare_compute_graph(get_graph, &graph)) {
|
|
return std::nullopt;
|
|
}
|
|
params_tensor_set_dirty_ = true;
|
|
rebuild_params_tensor_set();
|
|
if (auto manager = residency_manager.lock()) {
|
|
for (int i = 0; i < sd::ggml_graph_cut::leaf_count(graph); ++i) {
|
|
auto* parameter = manager->resolve_param_tensor(sd::ggml_graph_cut::leaf_tensor(graph, i));
|
|
if (parameter != nullptr)
|
|
params_tensor_set_.insert(parameter);
|
|
}
|
|
}
|
|
std::optional<sd::Tensor<float>> output;
|
|
try {
|
|
output = execute_graph(graph, n_threads, no_return, read_outputs);
|
|
} catch (const std::exception& error) {
|
|
last_compute_status_ = GGML_STATUS_FAILED;
|
|
LOG_ERROR("%s graph execution failed on %s: %s", get_desc().c_str(),
|
|
ggml_backend_name(runtime_backend), error.what());
|
|
return std::nullopt;
|
|
}
|
|
success = output.has_value();
|
|
if (success) {
|
|
cache_.graph_end(true);
|
|
last_compute_status_ = GGML_STATUS_SUCCESS;
|
|
}
|
|
return output;
|
|
}
|
|
|
|
void GGMLRunner::set_graph_cut_layer_split_enabled(bool enabled) {
|
|
graph_cut_layer_split_enabled = enabled;
|
|
if (!enabled) {
|
|
graph_cut_layer_split_assignments_.clear();
|
|
graph_cut_layer_split_node_assignments_.clear();
|
|
graph_cut_layer_split_primary_notice_logged_ = false;
|
|
}
|
|
}
|
|
|
|
void GGMLRunner::set_graph_cut_layer_split_backend_vram_limits(const std::vector<size_t>& limits) {
|
|
graph_cut_layer_split_backend_vram_limits_ = limits;
|
|
graph_cut_layer_split_assignments_.clear();
|
|
graph_cut_layer_split_node_assignments_.clear();
|
|
graph_cut_layer_split_primary_notice_logged_ = false;
|
|
}
|
|
|
|
void GGMLRunner::set_runtime_backends(const std::vector<ggml_backend_t>& backends) {
|
|
extra_runtime_backends.clear();
|
|
for (ggml_backend_t backend : backends) {
|
|
if (backend == nullptr || backend == runtime_backend) {
|
|
continue;
|
|
}
|
|
if (std::find(extra_runtime_backends.begin(), extra_runtime_backends.end(), backend) ==
|
|
extra_runtime_backends.end()) {
|
|
extra_runtime_backends.push_back(backend);
|
|
}
|
|
}
|
|
workspace_.set_extra_backends(extra_runtime_backends);
|
|
graph_cut_layer_split_assignments_.clear();
|
|
graph_cut_layer_split_node_assignments_.clear();
|
|
graph_cut_layer_split_primary_notice_logged_ = false;
|
|
}
|
|
|
|
static size_t add_bytes(size_t a, size_t b) {
|
|
return b > SIZE_MAX - a ? SIZE_MAX : a + b;
|
|
}
|
|
|
|
ComputeWorkspace::Measurement GGMLRunner::measure(ggml_cgraph* graph, size_t direct_bytes) {
|
|
auto external_backend = [&](const ggml_tensor* tensor) -> ggml_backend_t {
|
|
if (!params_tensor_set_.count(tensor)) {
|
|
return nullptr;
|
|
}
|
|
auto placement = graph_cut_layer_split_assignments_.find(tensor);
|
|
return placement == graph_cut_layer_split_assignments_.end() ? runtime_backend : placement->second;
|
|
};
|
|
auto assign_nodes = [&](ggml_backend_sched_t scheduler, ggml_cgraph* copy) {
|
|
pin_multi_device_nodes(scheduler, copy, graph);
|
|
};
|
|
return workspace_.measure(graph, direct_bytes, external_backend, assign_nodes);
|
|
}
|
|
|
|
std::vector<DeviceMemoryRequest> GGMLRunner::memory_requests(
|
|
const std::vector<BackendBufferSize>& sizes,
|
|
size_t pending_cache_bytes) const {
|
|
std::vector<DeviceMemoryRequest> requests;
|
|
for (const auto& size : sizes) {
|
|
const size_t retained = retained_runtime_buffer_bytes(size.backend);
|
|
const size_t reusable = workspace_.bytes(size.backend);
|
|
const size_t cache_bytes = size.backend == runtime_backend ? pending_cache_bytes : 0;
|
|
const size_t pending = add_bytes(size.bytes > reusable ? size.bytes - reusable : 0, cache_bytes);
|
|
size_t limit = max_graph_vram_bytes;
|
|
if (is_multi_device()) {
|
|
size_t index = 0;
|
|
if (size.backend != runtime_backend) {
|
|
auto position = std::find(extra_runtime_backends.begin(), extra_runtime_backends.end(), size.backend);
|
|
index = static_cast<size_t>(position - extra_runtime_backends.begin()) + 1;
|
|
}
|
|
if (index < graph_cut_layer_split_backend_vram_limits_.size()) {
|
|
limit = graph_cut_layer_split_backend_vram_limits_[index];
|
|
}
|
|
}
|
|
requests.push_back({size.backend, reinterpret_cast<uintptr_t>(this), pending,
|
|
retained, limit});
|
|
}
|
|
return requests;
|
|
}
|
|
|
|
bool GGMLRunner::fits(const std::vector<DeviceMemoryRequest>& requests,
|
|
const std::vector<ggml_tensor*>& params) const {
|
|
auto manager = residency_manager.lock();
|
|
if (manager == nullptr) {
|
|
return params.empty();
|
|
}
|
|
for (const auto& request : requests) {
|
|
if (!manager->fits_compute_backend_capacity(request, params)) {
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool GGMLRunner::execute_segment(ggml_cgraph* graph, int n_threads) {
|
|
if (sd_backend_is_cpu(runtime_backend)) {
|
|
sd_backend_cpu_set_n_threads(runtime_backend, n_threads);
|
|
}
|
|
if (workspace_.cpu_backend() != nullptr) {
|
|
sd_backend_cpu_set_n_threads(workspace_.cpu_backend(), n_threads);
|
|
}
|
|
auto scheduler = workspace_.scheduler();
|
|
ggml_status status;
|
|
if (scheduler != nullptr) {
|
|
if (sd_get_backend_eval_callback() != nullptr && !multi_device_eval_callback_warned) {
|
|
LOG_WARN("%s: eval callback is not supported with the backend scheduler; ignoring", get_desc().c_str());
|
|
multi_device_eval_callback_warned = true;
|
|
}
|
|
status = ggml_backend_sched_graph_compute(scheduler, graph);
|
|
} else {
|
|
status = sd_backend_graph_compute_with_eval_callback(runtime_backend, graph,
|
|
sd_get_backend_eval_callback(),
|
|
sd_get_backend_eval_callback_data());
|
|
}
|
|
workspace_.synchronize();
|
|
if (status != GGML_STATUS_SUCCESS) {
|
|
last_compute_status_ = status;
|
|
LOG_ERROR("%s compute failed: %s", get_desc().c_str(), ggml_status_to_string(status));
|
|
return false;
|
|
}
|
|
const std::string description = get_desc();
|
|
if (!debug_tensors.empty()) {
|
|
std::unordered_set<const ggml_tensor*> graph_tensors;
|
|
const int leaf_count = ggml_graph_cut::leaf_count(graph);
|
|
const int node_count = ggml_graph_n_nodes(graph);
|
|
graph_tensors.reserve(static_cast<size_t>(leaf_count + node_count));
|
|
for (int index = 0; index < leaf_count; ++index) {
|
|
graph_tensors.insert(ggml_graph_cut::leaf_tensor(graph, index));
|
|
}
|
|
for (int index = 0; index < node_count; ++index) {
|
|
graph_tensors.insert(ggml_graph_node(graph, index));
|
|
}
|
|
|
|
for (const auto& entry : debug_tensors) {
|
|
ggml_tensor* tensor = entry.first;
|
|
if (tensor == nullptr || graph_tensors.find(tensor) == graph_tensors.end()) {
|
|
continue;
|
|
}
|
|
ggml_backend_buffer_t buffer =
|
|
tensor->view_src != nullptr ? tensor->view_src->buffer : tensor->buffer;
|
|
if (buffer == nullptr) {
|
|
LOG_WARN("%s skip debug tensor '%s': tensor buffer not set",
|
|
description.c_str(),
|
|
entry.second.c_str());
|
|
continue;
|
|
}
|
|
if (tensor->type != GGML_TYPE_F32) {
|
|
LOG_WARN("%s skip debug tensor '%s': only GGML_TYPE_F32 is supported, got %s",
|
|
description.c_str(),
|
|
entry.second.c_str(),
|
|
ggml_type_name(tensor->type));
|
|
continue;
|
|
}
|
|
auto debug_tensor = make_sd_tensor_from_ggml<float>(tensor);
|
|
print_sd_tensor(debug_tensor, false, entry.second.c_str());
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
std::optional<Tensor<float>> GGMLRunner::execute_graph(ggml_cgraph* graph, int n_threads, bool no_return, const std::function<bool()>& read_outputs) {
|
|
if (!assign_graph_cut_layer_split_backends(graph)) {
|
|
return std::nullopt;
|
|
}
|
|
const auto params = collect_used_param_tensors(graph);
|
|
const auto& cached_plan = resolve_graph_cut_plan(graph);
|
|
const auto full_measurement = measure(graph, cached_plan.compute_buffer_size);
|
|
if (full_measurement.buffers.empty()) {
|
|
last_compute_status_ = GGML_STATUS_ALLOC_FAILED;
|
|
return std::nullopt;
|
|
}
|
|
auto manager = residency_manager.lock();
|
|
const bool segmented = !is_multi_device() && !sd_backend_is_cpu(runtime_backend) &&
|
|
manager != nullptr && manager->segmented_compute_enabled() &&
|
|
cached_plan.valid && cached_plan.has_cuts && cached_plan.segments.size() > 1 &&
|
|
!fits(memory_requests(full_measurement.buffers, cache_.pending_bytes(graph)), params);
|
|
ggml_graph_cut::Plan monolithic_plan;
|
|
if (!segmented) {
|
|
monolithic_plan.segments.emplace_back();
|
|
auto& segment = monolithic_plan.segments.back();
|
|
segment.group_name = "graph";
|
|
segment.compute_buffer_size = cached_plan.compute_buffer_size;
|
|
segment.internal_node_indices.reserve(ggml_graph_n_nodes(graph));
|
|
segment.input_refs.reserve(ggml_graph_cut::leaf_count(graph));
|
|
for (int i = 0; i < ggml_graph_n_nodes(graph); ++i) {
|
|
segment.internal_node_indices.push_back(i);
|
|
}
|
|
for (int i = 0; i < ggml_graph_cut::leaf_count(graph); ++i) {
|
|
auto tensor = ggml_graph_cut::leaf_tensor(graph, i);
|
|
ggml_graph_cut::Segment::InputRef input;
|
|
input.leaf_index = i;
|
|
input.type = canonical_param_tensor(tensor) != nullptr
|
|
? ggml_graph_cut::Segment::INPUT_PARAM
|
|
: ggml_graph_cut::Segment::INPUT_EXTERNAL;
|
|
segment.input_refs.push_back(input);
|
|
}
|
|
}
|
|
const auto& plan = segmented ? cached_plan : monolithic_plan;
|
|
const bool segments_changed = plan.segments.size() != logged_segment_count_;
|
|
if (segments_changed && (segmented || logged_segment_count_ > 1)) {
|
|
LOG_VERBOSE("%s using %zu segment%s", get_desc().c_str(),
|
|
plan.segments.size(), plan.segments.size() == 1 ? "" : "s");
|
|
}
|
|
SegmentGraphBindings bindings(cut_cache_, plan, graph);
|
|
SegmentWeightPipeline weights(manager, runtime_backend, reinterpret_cast<uintptr_t>(this),
|
|
graph, plan, params_tensor_set_,
|
|
segmented && manager != nullptr && manager->prefetch_enabled());
|
|
|
|
std::map<ggml_backend_t, size_t> peak_compute_bytes;
|
|
auto track_compute_buffer = [&](ggml_backend_t backend) {
|
|
if (backend != nullptr) {
|
|
auto& peak = peak_compute_bytes[backend];
|
|
peak = std::max(peak, workspace_.bytes(backend));
|
|
}
|
|
};
|
|
std::optional<Tensor<float>> output = Tensor<float>();
|
|
for (size_t index = 0; index < plan.segments.size(); ++index) {
|
|
const auto& segment = plan.segments[index];
|
|
const bool last = index + 1 == plan.segments.size();
|
|
auto fail_segment = [&](const char* phase) {
|
|
LOG_ERROR("%s segment %zu/%zu (%s) failed during %s", get_desc().c_str(),
|
|
index + 1, plan.segments.size(), segment.group_name.c_str(), phase);
|
|
return std::nullopt;
|
|
};
|
|
cut_cache_.prune(segment.live_cut_names);
|
|
bindings.reset(segment);
|
|
if (!bindings.bind_cached_inputs(segment, get_desc().c_str())) {
|
|
return fail_segment("input binding");
|
|
}
|
|
ggml_context* segment_context = nullptr;
|
|
auto segment_graph = segmented
|
|
? ggml_graph_cut::build_segment_graph(graph, segment, &segment_context)
|
|
: graph;
|
|
struct SegmentCleanup {
|
|
GGMLRunner& runner;
|
|
SegmentWeightPipeline& weights;
|
|
SegmentGraphBindings& bindings;
|
|
ggml_context* context;
|
|
~SegmentCleanup() {
|
|
if (!runner.workspace_.segment_end()) {
|
|
runner.last_compute_status_ = GGML_STATUS_FAILED;
|
|
}
|
|
bindings.restore();
|
|
weights.segment_end();
|
|
ggml_free(context);
|
|
runner.sync_runtime_residency();
|
|
}
|
|
} segment_cleanup{*this, weights, bindings, segment_context};
|
|
|
|
auto measurement = segmented ? measure(segment_graph, segment.compute_buffer_size) : full_measurement;
|
|
if (!workspace_.prepare(measurement)) {
|
|
last_compute_status_ = GGML_STATUS_ALLOC_FAILED;
|
|
return fail_segment("workspace preparation");
|
|
}
|
|
const size_t cut_bytes = last ? 0 : cut_cache_.estimate_output_bytes(graph, segment);
|
|
const size_t new_cache_bytes = add_bytes(cut_bytes, cache_.pending_bytes(segment_graph));
|
|
auto ensure_capacity = [&]() {
|
|
sync_runtime_residency();
|
|
auto requests = memory_requests(measurement.buffers, new_cache_bytes);
|
|
if (fits(requests, weights.params(index))) {
|
|
return true;
|
|
}
|
|
if (workspace_.release_excess(measurement)) {
|
|
sync_runtime_residency();
|
|
requests = memory_requests(measurement.buffers, new_cache_bytes);
|
|
}
|
|
const bool ready = weights.ensure_segment_capacity(index, requests);
|
|
if (!ready && manager != nullptr) {
|
|
last_compute_status_ = GGML_STATUS_ALLOC_FAILED;
|
|
}
|
|
return ready;
|
|
};
|
|
if (!weights.segment_start(index, ensure_capacity)) {
|
|
return fail_segment("weight preparation");
|
|
}
|
|
// Preparing weights can execute LoRA graphs and reclaim an idle workspace.
|
|
if (!workspace_.measurement_matches(segment_graph, measurement)) {
|
|
measurement = measure(segment_graph, segment.compute_buffer_size);
|
|
}
|
|
if (!workspace_.prepare(measurement)) {
|
|
last_compute_status_ = GGML_STATUS_ALLOC_FAILED;
|
|
return fail_segment("workspace preparation");
|
|
}
|
|
if (!ensure_capacity()) {
|
|
return fail_segment("workspace capacity check");
|
|
}
|
|
if (!workspace_.allocate(segment_graph, [&](ggml_backend_sched_t scheduler, ggml_cgraph* current) {
|
|
pin_multi_device_nodes(scheduler, current);
|
|
})) {
|
|
last_compute_status_ = GGML_STATUS_ALLOC_FAILED;
|
|
return fail_segment("workspace allocation");
|
|
}
|
|
for (const auto& size : measurement.buffers) {
|
|
track_compute_buffer(size.backend);
|
|
}
|
|
if (workspace_.scheduler() != nullptr) {
|
|
track_compute_buffer(workspace_.cpu_backend());
|
|
}
|
|
if (!ensure_capacity()) {
|
|
return fail_segment("allocated capacity check");
|
|
}
|
|
copy_data_to_backend_tensor(segment_graph, false);
|
|
auto prefetch_requests = memory_requests(measurement.buffers, new_cache_bytes);
|
|
if (!prefetch_requests.empty()) {
|
|
weights.enqueue_next(index, prefetch_requests.front());
|
|
}
|
|
LOG_DEBUG("%s executing segment %zu/%zu: %s", get_desc().c_str(),
|
|
index + 1, plan.segments.size(), segment.group_name.c_str());
|
|
if (!execute_segment(segment_graph, n_threads) ||
|
|
!cache_.capture(segment_graph) ||
|
|
!cut_cache_.capture(graph, segment, get_desc().c_str())) {
|
|
return fail_segment("execution or output caching");
|
|
}
|
|
sync_runtime_residency();
|
|
if (last) {
|
|
if (read_outputs && !read_outputs()) {
|
|
return fail_segment("output finalization");
|
|
}
|
|
if (!no_return) {
|
|
auto result = ggml_get_tensor(compute_ctx, final_result_name.c_str());
|
|
output = read_graph_tensor(result, "output");
|
|
if (!output.has_value()) {
|
|
return fail_segment("output readback");
|
|
}
|
|
}
|
|
}
|
|
if (!workspace_.segment_end()) {
|
|
last_compute_status_ = GGML_STATUS_FAILED;
|
|
return fail_segment("workspace synchronization");
|
|
}
|
|
// Final outputs and their callbacks may still be views of consumed cuts.
|
|
cut_cache_.prune(segment.future_cut_names);
|
|
}
|
|
if (segments_changed || peak_compute_bytes != logged_compute_bytes_) {
|
|
for (const auto& entry : peak_compute_bytes) {
|
|
LOG_VERBOSE("%s compute buffer size: %.2f MB(%s) on %s (peak across %zu segment%s)",
|
|
get_desc().c_str(), entry.second / (1024.0 * 1024.0),
|
|
sd_backend_is_cpu(entry.first) ? "RAM" : "VRAM", ggml_backend_name(entry.first),
|
|
plan.segments.size(), plan.segments.size() == 1 ? "" : "s");
|
|
}
|
|
logged_compute_bytes_ = std::move(peak_compute_bytes);
|
|
logged_segment_count_ = plan.segments.size();
|
|
}
|
|
return output;
|
|
}
|