upstream-archive/stable-diffusion.cpp
1
0
Fork 0
mirror of https://github.com/leejet/stable-diffusion.cpp.git synced 2026-09-26 16:07:50 -05:00
Code Issues Packages Projects Releases Wiki Activity

feat: add Qwen Image 2.1 prefix KV cache (#2035)

Browse Source
This commit is contained in:
leejet
2026-09-23 23:08:21 +08:00
committed by GitHub
parent e6281b6318
commit 2dc7f5408a
9 changed files with 237 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/core/runner_cache.cpp
+18 -12
View File
@@ -26,10 +26,13 @@ namespace sd {
std::unique_ptr<CachedTensor> CachedTensor::copy(ggml_backend_t backend,
const std::string& name,
ggml_tensor* source) {
ggml_tensor* source,
ggml_status& status) {
status = GGML_STATUS_FAILED;
if (ggml_graph_cut::tensor_buffer(source) == nullptr) {
return nullptr;
}
status = GGML_STATUS_ALLOC_FAILED;
auto entry = std::make_unique<CachedTensor>();
entry->context = ggml_init({2 * ggml_tensor_overhead(), nullptr, true});
if (entry->context == nullptr) {
@@ -50,6 +53,7 @@ namespace sd {
} else {
ggml_backend_tensor_copy(source, entry->tensor);
}
status = GGML_STATUS_SUCCESS;
return entry;
}
@@ -106,9 +110,9 @@ namespace sd {
return pending > SIZE_MAX - committed ? SIZE_MAX : committed + pending;
}
bool RunnerCache::capture(ggml_cgraph* graph) {
ggml_status RunnerCache::capture(ggml_cgraph* graph) {
if (outputs_.empty()) {
return true;
return GGML_STATUS_SUCCESS;
}
const auto tensors = cache_graph_tensors(graph);
for (const auto& output : outputs_) {
@@ -116,14 +120,15 @@ namespace sd {
continue;
}
GGML_ASSERT(ggml_is_contiguous(output.second));
auto entry = CachedTensor::copy(backend_, output.first, output.second);
ggml_status status;
auto entry = CachedTensor::copy(backend_, output.first, output.second, status);
if (entry == nullptr) {
return false;
return status;
}
pending_[output.first] = std::move(entry);
}
ggml_backend_synchronize(backend_);
return true;
return GGML_STATUS_SUCCESS;
}
void RunnerCache::graph_end(bool success) {
@@ -180,9 +185,9 @@ namespace sd {
}
}
bool GraphCutTensorCache::capture(ggml_cgraph* graph,
const ggml_graph_cut::Segment& segment,
const char* log_desc) {
ggml_status GraphCutTensorCache::capture(ggml_cgraph* graph,
const ggml_graph_cut::Segment& segment,
const char* log_desc) {
size_t copied_bytes = 0;
size_t copied_count = 0;
for (int index : segment.output_node_indices) {
@@ -191,10 +196,11 @@ namespace sd {
!segment.future_cut_names.count(output->name)) {
continue;
}
auto entry = CachedTensor::copy(backend_, output->name, ggml_graph_cut::cache_source_tensor(output));
ggml_status status;
auto entry = CachedTensor::copy(backend_, output->name, ggml_graph_cut::cache_source_tensor(output), status);
if (entry == nullptr) {
LOG_ERROR("%s failed to capture graph cut tensor: %s", log_desc, output->name);
return false;
return status;
}
const size_t size = ggml_backend_buffer_get_size(entry->buffer);
copied_bytes = size > SIZE_MAX - copied_bytes ? SIZE_MAX : copied_bytes + size;
@@ -206,6 +212,6 @@ namespace sd {
LOG_DEBUG("%s graph cut cache added %6.2f MB (%zu tensors)",
log_desc, copied_bytes / (1024.f * 1024.f), copied_count);
}
return true;
return GGML_STATUS_SUCCESS;
}
}