DarthAffe/stable-diffusion.cpp
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fix: handle GPU memory reports and LLM encoding failures (#2020)

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leejet 2026-09-22 00:28:52 +08:00 committed by GitHub
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commit 6dcb5bbd42
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4 changed files with 25 additions and 10 deletions
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7
docs/backend.md
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@ -156,8 +156,11 @@ the runner's graph-cut capacity checks.
Runtime capacity checks also leave 512 MiB of currently free device memory for
backend scratch buffers and pipelines, including with explicit backend assignments.
They cap stale free-memory reports by the device's total memory minus tracked
resident allocations and reject reports that exceed the device's total memory.
They cap free-memory reports by the device's total memory minus tracked
resident allocations. Vulkan reports exceeding total memory are rejected because
its heap-budget subtraction can underflow. Other backends use the cap instead of
treating such reports as zero free memory. Failed checks log the reported free and
total memory alongside tracked weight and runtime allocations.
Components are considered in `diffusion`, `te`, `vae` order so that repeatedly
used diffusion weights have priority. Each component's weights use the first

5
src/conditioning/conditioner.hpp
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@ -2219,7 +2219,10 @@ struct LLMEmbedder : public Conditioner {
false,
deepstack_image_embeds,
image_grids);
GGML_ASSERT(!hidden_states.empty());
if (hidden_states.empty()) {
LOG_ERROR("LLM prompt encoding failed");
return {};
}
hidden_states = apply_token_weights(std::move(hidden_states), weights);
GGML_ASSERT(hidden_states.shape()[1] > prompt_template_encode_start_idx);

20
src/model_manager.cpp
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@ -1613,7 +1613,8 @@ void ModelManager::remove_runtime_owner(uintptr_t owner_id) {
ModelManager::CapacityCheck ModelManager::check_capacity(
const DeviceMemoryRequest& request,
const std::vector<TensorState*>& states) const {
const std::vector<TensorState*>& states,
bool log_details) const {
CapacityCheck result;
if (request.compute_backend == nullptr || sd_backend_is_cpu(request.compute_backend)) {
return result;
@ -1631,16 +1632,23 @@ ModelManager::CapacityCheck ModelManager::check_capacity(
}
size_t free_bytes = 0, total_bytes = 0;
ggml_backend_dev_memory(device, &free_bytes, &total_bytes);
const size_t weights_resident = compute_backend_resident_bytes(backend);
const size_t other_runtime = other_runtime_resident_bytes(request.owner_id, backend);
const size_t resident = add(weights_resident, add(other_runtime, request.runtime_resident_bytes));
if (log_details) {
LOG_WARN("model manager memory on %s: reported free %.2f MB / total %.2f MB, tracked weights %.2f MB / other runtime %.2f MB / current runtime %.2f MB",
ggml_backend_name(backend),
free_bytes / (1024.0 * 1024.0), total_bytes / (1024.0 * 1024.0),
weights_resident / (1024.0 * 1024.0), other_runtime / (1024.0 * 1024.0),
request.runtime_resident_bytes / (1024.0 * 1024.0));
}
if (free_bytes == 0 && total_bytes == 0) {
return SIZE_MAX;
}
// Vulkan's heap budget subtraction can underflow when usage exceeds the budget.
if (total_bytes > 0 && free_bytes > total_bytes) {
if (total_bytes > 0 && free_bytes > total_bytes && sd_backend_is(backend, "Vulkan")) {
return size_t{0};
}
const size_t resident = add(compute_backend_resident_bytes(backend),
add(other_runtime_resident_bytes(request.owner_id, backend),
request.runtime_resident_bytes));
if (total_bytes > 0) {
free_bytes = std::min(free_bytes, resident < total_bytes ? total_bytes - resident : 0);
}
@ -1786,7 +1794,7 @@ bool ModelManager::ensure_compute_backend_capacity(
}
}
const auto capacity = check_capacity(request, required_states);
const auto capacity = check_capacity(request, required_states, true);
const std::string available_device = capacity.available_device_bytes == SIZE_MAX
? "unknown"
: sd_format("%.2f MB", capacity.available_device_bytes / (1024.0 * 1024.0));

3
src/model_manager.h
View File

@ -157,7 +157,8 @@ private:
}
};
CapacityCheck check_capacity(const DeviceMemoryRequest& request,
const std::vector<TensorState*>& states) const;
const std::vector<TensorState*>& states,
bool log_details = false) const;
ggml_backend_buffer_type_t params_buffer_type_for(const TensorState& state) const;
ggml_backend_buffer_type_t split_buffer_type_for(const TensorState& state) const;