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Copy pathggml_graph_cut.cpp
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756 lines (676 loc) · 30.6 KB
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#include "ggml_graph_cut.h"
#include <algorithm>
#include <cstring>
#include <map>
#include <set>
#include <sstream>
#include <stack>
#include <unordered_map>
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "util.h"
#include "../ggml/src/ggml-impl.h"
namespace sd::ggml_graph_cut {
static constexpr double MAX_VRAM_BYTES_PER_GIB = 1024.0 * 1024.0 * 1024.0;
static std::string graph_cut_tensor_display_name(const ggml_tensor* tensor) {
if (tensor == nullptr) {
return "<null>";
}
if (tensor->name[0] != '\0') {
return tensor->name;
}
return sd_format("<tensor@%p>", (const void*)tensor);
}
static int graph_leaf_index(ggml_cgraph* gf, const ggml_tensor* tensor) {
GGML_ASSERT(gf != nullptr);
GGML_ASSERT(tensor != nullptr);
for (int i = 0; i < gf->n_leafs; ++i) {
if (gf->leafs[i] == tensor) {
return i;
}
}
return -1;
}
static bool is_params_tensor(const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const ggml_tensor* tensor) {
if (tensor == nullptr) {
return false;
}
return params_tensor_set.find(tensor) != params_tensor_set.end();
}
static int graph_node_index_by_name(ggml_cgraph* gf, const char* name) {
GGML_ASSERT(gf != nullptr);
if (name == nullptr || name[0] == '\0') {
return -1;
}
const int n_nodes = ggml_graph_n_nodes(gf);
for (int i = 0; i < n_nodes; ++i) {
ggml_tensor* node = ggml_graph_node(gf, i);
if (node != nullptr && std::strcmp(node->name, name) == 0) {
return i;
}
}
return -1;
}
static Plan::InputShape input_shape(const ggml_tensor* tensor) {
Plan::InputShape shape;
if (tensor == nullptr) {
return shape;
}
shape.type = tensor->type;
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
shape.ne[static_cast<size_t>(i)] = tensor->ne[i];
}
return shape;
}
static size_t graph_cut_segment_vram_bytes(const Segment& segment) {
return segment.compute_buffer_size +
segment.input_param_bytes +
segment.input_previous_cut_bytes +
segment.output_bytes;
}
size_t max_vram_gib_to_bytes(float max_vram) {
if (max_vram <= 0.f) {
return 0;
}
return static_cast<size_t>(static_cast<double>(max_vram) * MAX_VRAM_BYTES_PER_GIB);
}
static float max_vram_bytes_to_gib(size_t max_vram_bytes) {
return static_cast<float>(static_cast<double>(max_vram_bytes) / MAX_VRAM_BYTES_PER_GIB);
}
static size_t resolve_auto_max_vram_bytes(float spare_vram, ggml_backend_t backend) {
if (backend == nullptr) {
LOG_WARN("--max-vram < 0 requested, but no backend is available; disabling graph splitting");
return 0;
}
ggml_backend_dev_t dev = ggml_backend_get_device(backend);
if (dev == nullptr) {
LOG_WARN("--max-vram < 0 requested, but no backend device is available; disabling graph splitting");
return 0;
}
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
LOG_WARN("--max-vram < 0 requested, but the main backend is CPU; disabling graph splitting");
return 0;
}
size_t free_vram = 0;
size_t total_vram = 0;
ggml_backend_dev_memory(dev, &free_vram, &total_vram);
size_t spare_bytes = static_cast<size_t>(MAX_VRAM_BYTES_PER_GIB * spare_vram);
if (free_vram <= spare_bytes) {
LOG_WARN("--max-vram < 0 requested, but free VRAM is %.2f GiB; reserving %.2f GiB leaves no graph budget",
free_vram / MAX_VRAM_BYTES_PER_GIB, spare_vram);
return 0;
}
const size_t max_vram_bytes = free_vram - spare_bytes;
LOG_INFO("--max-vram < 0 auto-detected %.2f GiB free VRAM (%.2f GiB total), reserving %.2f GiB; using %.2f GiB",
free_vram / MAX_VRAM_BYTES_PER_GIB,
total_vram / MAX_VRAM_BYTES_PER_GIB,
spare_vram,
max_vram_bytes / MAX_VRAM_BYTES_PER_GIB);
return max_vram_bytes;
}
float resolve_max_vram_gib(float max_vram, ggml_backend_t backend) {
if (max_vram >= 0.f) {
return max_vram;
}
return max_vram_bytes_to_gib(resolve_auto_max_vram_bytes(-max_vram, backend));
}
static Segment make_segment_seed(const Plan& plan,
size_t start_segment_index,
size_t end_segment_index) {
GGML_ASSERT(start_segment_index < plan.segments.size());
GGML_ASSERT(end_segment_index < plan.segments.size());
GGML_ASSERT(start_segment_index <= end_segment_index);
Segment seed;
const auto& start_segment = plan.segments[start_segment_index];
const auto& target_segment = plan.segments[end_segment_index];
std::unordered_set<int> seen_output_node_indices;
for (size_t seg_idx = start_segment_index; seg_idx <= end_segment_index; ++seg_idx) {
for (int output_node_index : plan.segments[seg_idx].output_node_indices) {
if (seen_output_node_indices.insert(output_node_index).second) {
seed.output_node_indices.push_back(output_node_index);
}
}
}
if (start_segment_index == end_segment_index) {
seed.group_name = target_segment.group_name;
} else {
seed.group_name = sd_format("%s..%s",
start_segment.group_name.c_str(),
target_segment.group_name.c_str());
}
return seed;
}
static void build_segment(ggml_cgraph* gf,
Plan& plan,
Segment& segment,
const std::unordered_map<const ggml_tensor*, int>& producer_index,
std::unordered_set<int>& available_cut_output_node_indices,
ggml_backend_t backend,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc) {
std::set<int> internal_nodes;
std::unordered_set<const ggml_tensor*> input_seen;
std::vector<Segment::InputRef> input_refs;
std::stack<ggml_tensor*> work_stack;
for (int output_node_index : segment.output_node_indices) {
ggml_tensor* output = ggml_graph_node(gf, output_node_index);
if (output != nullptr) {
work_stack.push(output);
}
}
while (!work_stack.empty()) {
ggml_tensor* tensor = work_stack.top();
work_stack.pop();
if (tensor == nullptr) {
continue;
}
auto producer_it = producer_index.find(tensor);
if (producer_it == producer_index.end()) {
if (input_seen.insert(tensor).second) {
Segment::InputRef input_ref;
input_ref.type = is_params_tensor(params_tensor_set, tensor) ? Segment::INPUT_PARAM : Segment::INPUT_EXTERNAL;
input_ref.display_name = graph_cut_tensor_display_name(tensor);
input_ref.leaf_index = graph_leaf_index(gf, tensor);
input_refs.push_back(std::move(input_ref));
}
continue;
}
int node_idx = producer_it->second;
if (available_cut_output_node_indices.find(node_idx) != available_cut_output_node_indices.end()) {
if (input_seen.insert(tensor).second) {
Segment::InputRef input_ref;
input_ref.type = Segment::INPUT_PREVIOUS_CUT;
input_ref.display_name = graph_cut_tensor_display_name(tensor);
input_ref.node_index = node_idx;
input_refs.push_back(std::move(input_ref));
}
continue;
}
if (!internal_nodes.insert(node_idx).second) {
continue;
}
ggml_tensor* node = ggml_graph_node(gf, node_idx);
for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) {
if (node->src[src_idx] != nullptr) {
work_stack.push(node->src[src_idx]);
}
}
}
if (!internal_nodes.empty()) {
segment.internal_node_indices.assign(internal_nodes.begin(), internal_nodes.end());
}
std::sort(input_refs.begin(),
input_refs.end(),
[](const Segment::InputRef& a, const Segment::InputRef& b) {
if (a.type != b.type) {
return a.type < b.type;
}
return a.display_name < b.display_name;
});
segment.input_refs = input_refs;
for (const auto& input : input_refs) {
ggml_tensor* current_input = input_tensor(gf, input);
size_t tensor_bytes = current_input == nullptr
? 0
: (input.type == Segment::INPUT_PREVIOUS_CUT
? cache_tensor_bytes(current_input)
: ggml_nbytes(current_input));
switch (input.type) {
case Segment::INPUT_PREVIOUS_CUT:
segment.input_previous_cut_bytes += tensor_bytes;
break;
case Segment::INPUT_PARAM:
segment.input_param_bytes += tensor_bytes;
break;
case Segment::INPUT_EXTERNAL:
default:
segment.input_external_bytes += tensor_bytes;
break;
}
}
for (int output_node_index : segment.output_node_indices) {
ggml_tensor* output = ggml_graph_node(gf, output_node_index);
segment.output_bytes += cache_tensor_bytes(output);
}
segment.compute_buffer_size = measure_segment_compute_buffer(backend, gf, segment, log_desc);
for (int output_node_index : segment.output_node_indices) {
available_cut_output_node_indices.insert(output_node_index);
}
plan.segments.push_back(std::move(segment));
}
bool is_graph_cut_tensor(const ggml_tensor* tensor) {
if (tensor == nullptr || tensor->name[0] == '\0') {
return false;
}
return std::strncmp(tensor->name, GGML_RUNNER_CUT_PREFIX, std::strlen(GGML_RUNNER_CUT_PREFIX)) == 0;
}
std::string make_graph_cut_name(const std::string& group, const std::string& output) {
return std::string(GGML_RUNNER_CUT_PREFIX) + group + "|" + output;
}
void mark_graph_cut(ggml_tensor* tensor, const std::string& group, const std::string& output) {
if (tensor == nullptr) {
return;
}
auto name = make_graph_cut_name(group, output);
ggml_set_name(tensor, name.c_str());
}
int leaf_count(ggml_cgraph* gf) {
GGML_ASSERT(gf != nullptr);
return gf->n_leafs;
}
ggml_tensor* leaf_tensor(ggml_cgraph* gf, int leaf_index) {
GGML_ASSERT(gf != nullptr);
if (leaf_index < 0 || leaf_index >= gf->n_leafs) {
return nullptr;
}
return gf->leafs[leaf_index];
}
ggml_backend_buffer_t tensor_buffer(const ggml_tensor* tensor) {
if (tensor == nullptr) {
return nullptr;
}
return tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
}
ggml_tensor* cache_source_tensor(ggml_tensor* tensor) {
if (tensor == nullptr) {
return nullptr;
}
if (tensor_buffer(tensor) == nullptr && tensor->src[0] != nullptr &&
ggml_nelements(tensor->src[0]) == ggml_nelements(tensor) &&
ggml_nbytes(tensor->src[0]) == ggml_nbytes(tensor)) {
return cache_source_tensor(tensor->src[0]);
}
return tensor->view_src ? tensor->view_src : tensor;
}
size_t cache_tensor_bytes(const ggml_tensor* tensor) {
if (tensor == nullptr) {
return 0;
}
const ggml_tensor* cache_src = tensor->view_src ? tensor->view_src : tensor;
return ggml_nbytes(cache_src);
}
bool plan_matches_graph(ggml_cgraph* gf, const Plan& plan) {
GGML_ASSERT(gf != nullptr);
if (ggml_graph_n_nodes(gf) != plan.n_nodes || gf->n_leafs != plan.n_leafs) {
return false;
}
for (const auto& input_shape_ref : plan.input_shapes) {
if (input_shape_ref.leaf_index < 0 || input_shape_ref.leaf_index >= gf->n_leafs) {
return false;
}
ggml_tensor* leaf = gf->leafs[input_shape_ref.leaf_index];
if (leaf == nullptr || input_shape_ref.type != leaf->type) {
return false;
}
for (int d = 0; d < GGML_MAX_DIMS; ++d) {
if (input_shape_ref.ne[static_cast<size_t>(d)] != leaf->ne[d]) {
return false;
}
}
}
return true;
}
ggml_tensor* output_tensor(ggml_cgraph* gf, const Segment& segment, size_t output_index) {
GGML_ASSERT(gf != nullptr);
if (output_index >= segment.output_node_indices.size()) {
return nullptr;
}
int node_index = segment.output_node_indices[output_index];
if (node_index < 0 || node_index >= ggml_graph_n_nodes(gf)) {
return nullptr;
}
return ggml_graph_node(gf, node_index);
}
ggml_tensor* input_tensor(ggml_cgraph* gf, const Segment::InputRef& input_ref) {
GGML_ASSERT(gf != nullptr);
if (input_ref.type == Segment::INPUT_PREVIOUS_CUT) {
if (input_ref.node_index < 0 || input_ref.node_index >= ggml_graph_n_nodes(gf)) {
return nullptr;
}
return ggml_graph_node(gf, input_ref.node_index);
}
if (input_ref.leaf_index < 0 || input_ref.leaf_index >= gf->n_leafs) {
return nullptr;
}
return leaf_tensor(gf, input_ref.leaf_index);
}
std::vector<ggml_tensor*> param_tensors(ggml_cgraph* gf, const Segment& segment) {
GGML_ASSERT(gf != nullptr);
std::vector<ggml_tensor*> tensors;
std::unordered_set<ggml_tensor*> seen_tensors;
tensors.reserve(segment.input_refs.size());
seen_tensors.reserve(segment.input_refs.size());
for (const auto& input_ref : segment.input_refs) {
if (input_ref.type != Segment::INPUT_PARAM) {
continue;
}
ggml_tensor* tensor = input_tensor(gf, input_ref);
if (tensor == nullptr) {
continue;
}
if (seen_tensors.insert(tensor).second) {
tensors.push_back(tensor);
}
}
return tensors;
}
std::vector<ggml_tensor*> runtime_param_tensors(ggml_cgraph* gf, const Segment& segment, const char* log_desc) {
std::vector<ggml_tensor*> tensors = param_tensors(gf, segment);
std::vector<ggml_tensor*> filtered_tensors;
filtered_tensors.reserve(tensors.size());
for (ggml_tensor* tensor : tensors) {
if (tensor_buffer(tensor) == nullptr) {
LOG_WARN("%s graph cut skipping param input without buffer: segment=%s tensor=%s",
log_desc == nullptr ? "unknown" : log_desc,
segment.group_name.c_str(),
tensor->name);
continue;
}
filtered_tensors.push_back(tensor);
}
return filtered_tensors;
}
std::unordered_set<std::string> collect_future_input_names(ggml_cgraph* gf,
const Plan& plan,
size_t current_segment_index) {
GGML_ASSERT(gf != nullptr);
std::unordered_set<std::string> future_input_names;
for (size_t seg_idx = current_segment_index + 1; seg_idx < plan.segments.size(); ++seg_idx) {
const auto& segment = plan.segments[seg_idx];
for (const auto& input_ref : segment.input_refs) {
if (input_ref.type != Segment::INPUT_PREVIOUS_CUT) {
continue;
}
ggml_tensor* current_input = input_tensor(gf, input_ref);
if (current_input != nullptr && current_input->name[0] != '\0') {
future_input_names.insert(current_input->name);
}
}
}
return future_input_names;
}
ggml_cgraph* build_segment_graph(ggml_cgraph* gf,
const Segment& segment,
ggml_context** graph_ctx_out) {
GGML_ASSERT(gf != nullptr);
GGML_ASSERT(graph_ctx_out != nullptr);
const size_t graph_size = segment.internal_node_indices.size() + segment.input_refs.size() + 8;
ggml_init_params params = {
/*.mem_size =*/ggml_graph_overhead_custom(graph_size, false) + 1024,
/*.mem_buffer =*/nullptr,
/*.no_alloc =*/true,
};
ggml_context* graph_ctx = ggml_init(params);
GGML_ASSERT(graph_ctx != nullptr);
ggml_cgraph* segment_graph = ggml_new_graph_custom(graph_ctx, graph_size, false);
GGML_ASSERT(segment_graph != nullptr);
for (const auto& input : segment.input_refs) {
ggml_tensor* current_input = input_tensor(gf, input);
if (current_input == nullptr) {
continue;
}
GGML_ASSERT(segment_graph->n_leafs < segment_graph->size);
segment_graph->leafs[segment_graph->n_leafs++] = current_input;
}
for (int output_node_index : segment.output_node_indices) {
ggml_tensor* output = ggml_graph_node(gf, output_node_index);
if (output == nullptr) {
continue;
}
ggml_set_output(output);
}
for (int node_idx : segment.internal_node_indices) {
ggml_graph_add_node(segment_graph, ggml_graph_node(gf, node_idx));
}
*graph_ctx_out = graph_ctx;
return segment_graph;
}
size_t measure_segment_compute_buffer(ggml_backend_t backend,
ggml_cgraph* gf,
const Segment& segment,
const char* log_desc) {
GGML_ASSERT(backend != nullptr);
GGML_ASSERT(gf != nullptr);
if (segment.internal_node_indices.empty()) {
return 0;
}
ggml_context* graph_ctx = nullptr;
ggml_cgraph* segment_graph = build_segment_graph(gf, segment, &graph_ctx);
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
size_t sizes[1] = {0};
ggml_gallocr_reserve_n_size(
allocr,
segment_graph,
nullptr,
nullptr,
sizes);
size_t buffer_size = sizes[0];
ggml_gallocr_free(allocr);
ggml_free(graph_ctx);
return buffer_size;
}
Plan build_plan(ggml_backend_t backend,
ggml_cgraph* gf,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc) {
GGML_ASSERT(backend != nullptr);
GGML_ASSERT(gf != nullptr);
Plan plan;
plan.available = true;
const int n_nodes = ggml_graph_n_nodes(gf);
if (n_nodes <= 0) {
return plan;
}
plan.n_nodes = n_nodes;
plan.n_leafs = gf->n_leafs;
for (int i = 0; i < gf->n_leafs; ++i) {
ggml_tensor* leaf = gf->leafs[i];
if (is_params_tensor(params_tensor_set, leaf)) {
continue;
}
auto shape = input_shape(leaf);
shape.leaf_index = i;
plan.input_shapes.push_back(shape);
}
std::unordered_map<const ggml_tensor*, int> producer_index;
producer_index.reserve(static_cast<size_t>(n_nodes));
for (int i = 0; i < n_nodes; ++i) {
producer_index[ggml_graph_node(gf, i)] = i;
}
std::vector<Segment> grouped_segments;
std::unordered_map<std::string, size_t> group_to_segment;
for (int i = 0; i < n_nodes; ++i) {
ggml_tensor* node = ggml_graph_node(gf, i);
if (!is_graph_cut_tensor(node)) {
continue;
}
plan.has_cuts = true;
std::string full_name(node->name);
std::string payload = full_name.substr(std::strlen(GGML_RUNNER_CUT_PREFIX));
size_t sep = payload.find('|');
std::string group = sep == std::string::npos ? payload : payload.substr(0, sep);
auto it = group_to_segment.find(group);
if (it == group_to_segment.end()) {
Segment segment;
segment.group_name = group;
segment.output_node_indices.push_back(i);
group_to_segment[group] = grouped_segments.size();
grouped_segments.push_back(std::move(segment));
} else {
auto& segment = grouped_segments[it->second];
segment.output_node_indices.push_back(i);
}
}
if (!plan.has_cuts) {
return plan;
}
std::unordered_set<int> available_cut_output_node_indices;
available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));
for (auto& segment : grouped_segments) {
build_segment(gf,
plan,
segment,
producer_index,
available_cut_output_node_indices,
backend,
params_tensor_set,
log_desc);
}
int final_output_index = graph_node_index_by_name(gf, "ggml_runner_final_result_tensor");
if (final_output_index < 0) {
final_output_index = n_nodes - 1;
}
ggml_tensor* final_output = final_output_index >= 0 ? ggml_graph_node(gf, final_output_index) : nullptr;
if (final_output != nullptr && available_cut_output_node_indices.find(final_output_index) == available_cut_output_node_indices.end()) {
Segment final_segment;
final_segment.group_name = "ggml_runner.final";
final_segment.output_node_indices.push_back(final_output_index);
build_segment(gf,
plan,
final_segment,
producer_index,
available_cut_output_node_indices,
backend,
params_tensor_set,
log_desc);
}
return plan;
}
Plan apply_max_vram_budget(ggml_cgraph* gf,
const Plan& base_plan,
size_t max_graph_vram_bytes,
ggml_backend_t backend,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc) {
GGML_ASSERT(backend != nullptr);
GGML_ASSERT(gf != nullptr);
int64_t t_budget_begin = ggml_time_ms();
if (max_graph_vram_bytes == 0 || !base_plan.has_cuts || base_plan.segments.size() <= 1) {
return base_plan;
}
const int n_nodes = ggml_graph_n_nodes(gf);
std::unordered_map<const ggml_tensor*, int> producer_index;
producer_index.reserve(static_cast<size_t>(n_nodes));
for (int i = 0; i < n_nodes; ++i) {
producer_index[ggml_graph_node(gf, i)] = i;
}
Plan merged_plan;
merged_plan.available = true;
merged_plan.has_cuts = base_plan.has_cuts;
merged_plan.valid = base_plan.valid;
merged_plan.n_nodes = base_plan.n_nodes;
merged_plan.n_leafs = base_plan.n_leafs;
std::unordered_set<int> available_cut_output_node_indices;
available_cut_output_node_indices.reserve(static_cast<size_t>(n_nodes));
size_t start_segment_index = 0;
while (start_segment_index < base_plan.segments.size()) {
Plan single_plan;
auto single_available_cut_output_node_indices = available_cut_output_node_indices;
auto single_seed = make_segment_seed(base_plan,
start_segment_index,
start_segment_index);
build_segment(gf,
single_plan,
single_seed,
producer_index,
single_available_cut_output_node_indices,
backend,
params_tensor_set,
log_desc);
GGML_ASSERT(!single_plan.segments.empty());
size_t best_end_segment_index = start_segment_index;
bool can_merge_next_segment = graph_cut_segment_vram_bytes(single_plan.segments.back()) <= max_graph_vram_bytes;
while (can_merge_next_segment && best_end_segment_index + 1 < base_plan.segments.size()) {
const size_t next_end_segment_index = best_end_segment_index + 1;
Plan candidate_plan;
auto candidate_available_cut_output_node_indices = available_cut_output_node_indices;
auto candidate_seed = make_segment_seed(base_plan,
start_segment_index,
next_end_segment_index);
build_segment(gf,
candidate_plan,
candidate_seed,
producer_index,
candidate_available_cut_output_node_indices,
backend,
params_tensor_set,
log_desc);
GGML_ASSERT(!candidate_plan.segments.empty());
const auto& candidate_segment = candidate_plan.segments.back();
if (graph_cut_segment_vram_bytes(candidate_segment) > max_graph_vram_bytes) {
break;
}
best_end_segment_index = next_end_segment_index;
}
auto best_seed = make_segment_seed(base_plan,
start_segment_index,
best_end_segment_index);
build_segment(gf,
merged_plan,
best_seed,
producer_index,
available_cut_output_node_indices,
backend,
params_tensor_set,
log_desc);
start_segment_index = best_end_segment_index + 1;
}
if (log_desc != nullptr && merged_plan.segments.size() != base_plan.segments.size()) {
LOG_INFO("%s graph cut max_vram=%.2f MB merged %zu segments -> %zu segments",
log_desc,
max_graph_vram_bytes / 1024.0 / 1024.0,
base_plan.segments.size(),
merged_plan.segments.size());
}
if (log_desc != nullptr) {
LOG_INFO("%s graph cut max_vram budget merge took %lld ms",
log_desc,
ggml_time_ms() - t_budget_begin);
}
return merged_plan;
}
Plan resolve_plan(ggml_backend_t backend,
ggml_cgraph* gf,
PlanCache* cache,
size_t max_graph_vram_bytes,
const std::unordered_set<const ggml_tensor*>& params_tensor_set,
const char* log_desc) {
GGML_ASSERT(backend != nullptr);
GGML_ASSERT(gf != nullptr);
GGML_ASSERT(cache != nullptr);
int64_t t_prepare_begin = ggml_time_ms();
Plan base_plan;
int64_t t_plan_begin = ggml_time_ms();
if (cache->graph_cut_plan.available && plan_matches_graph(gf, cache->graph_cut_plan)) {
base_plan = cache->graph_cut_plan;
} else {
base_plan = build_plan(backend, gf, params_tensor_set, log_desc);
cache->graph_cut_plan = base_plan;
cache->graph_cut_plan.available = true;
cache->budgeted_graph_cut_plan.available = false;
if (log_desc != nullptr) {
LOG_INFO("%s build cached graph cut plan done (taking %lld ms)", log_desc, ggml_time_ms() - t_plan_begin);
}
}
Plan resolved_plan = base_plan;
if (max_graph_vram_bytes > 0 && base_plan.has_cuts) {
if (cache->budgeted_graph_cut_plan.available &&
cache->budgeted_graph_cut_plan_max_vram_bytes == max_graph_vram_bytes &&
plan_matches_graph(gf, cache->budgeted_graph_cut_plan)) {
resolved_plan = cache->budgeted_graph_cut_plan;
} else {
resolved_plan = apply_max_vram_budget(gf,
base_plan,
max_graph_vram_bytes,
backend,
params_tensor_set,
log_desc);
cache->budgeted_graph_cut_plan = resolved_plan;
cache->budgeted_graph_cut_plan.available = true;
cache->budgeted_graph_cut_plan_max_vram_bytes = max_graph_vram_bytes;
}
}
return resolved_plan;
}
} // namespace sd::ggml_graph_cut