mirror of
https://github.com/LostRuins/koboldcpp.git
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Merge commit '11b068d066' into concedo_experimental
# Conflicts: # CONTRIBUTING.md # docs/backend/SYCL.md # docs/install.md # docs/speculative.md # ggml/src/ggml-hip/CMakeLists.txt # ggml/src/ggml-opencl/ggml-opencl.cpp # ggml/src/ggml-sycl/common.hpp # ggml/src/ggml-sycl/element_wise.cpp # ggml/src/ggml-sycl/fattn-onednn.cpp # ggml/src/ggml-sycl/ggml-sycl.cpp # ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp # ggml/src/ggml-webgpu/ggml-webgpu.cpp # ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl # ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl # tests/test-backend-ops.cpp # tests/test-chat.cpp # tests/test-llama-archs.cpp # tools/cli/README.md # tools/llama-bench/llama-bench.cpp # tools/mtmd/CMakeLists.txt # tools/server/README.md
This commit is contained in:
Concedo
commit
98926f27c1
81 changed files with 5314 additions and 518 deletions
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@ -1062,6 +1062,31 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
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return devices;
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}
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void common_print_available_devices() {
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constexpr size_t MiB = 1024 * 1024;
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std::vector<ggml_backend_dev_t> devices;
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ggml_backend_load_all();
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for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
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auto * dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
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devices.push_back(dev);
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}
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}
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printf("Available devices:\n");
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if (devices.empty()) {
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printf(" (none)\n");
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return;
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}
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for (auto * dev : devices) {
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size_t free, total;
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ggml_backend_dev_memory(dev, &free, &total);
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printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / MiB, free / MiB);
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}
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}
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static void add_rpc_devices(const std::string & servers) {
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auto rpc_servers = string_split<std::string>(servers, ',');
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if (rpc_servers.empty()) {
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@ -2589,20 +2614,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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{"--list-devices"},
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"print list of available devices and exit",
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[](common_params &) {
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ggml_backend_load_all();
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std::vector<ggml_backend_dev_t> devices;
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for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
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auto * dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
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devices.push_back(dev);
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}
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}
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printf("Available devices:\n");
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for (auto * dev : devices) {
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size_t free, total;
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ggml_backend_dev_memory(dev, &free, &total);
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printf(" %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
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}
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common_print_available_devices();
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exit(0);
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}
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));
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@ -123,6 +123,9 @@ struct common_params_context {
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// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
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bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
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// load all backends and print the list of available (non-CPU) devices to stdout
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void common_print_available_devices();
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// parse input arguments from CLI into a map
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bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
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@ -1056,3 +1056,141 @@ void common_chat_peg_gemma4_mapper::visit(const common_peg_ast_arena & arena, co
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visit(arena, child_id);
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}
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}
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static void minimax_m3_collect(const common_peg_ast_arena & arena,
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const common_peg_ast_node & node,
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const std::string & tag,
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std::vector<common_peg_ast_id> & out) {
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for (auto child_id : node.children) {
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const auto & child = arena.get(child_id);
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if (child.tag == tag) {
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out.push_back(child_id);
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} else {
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minimax_m3_collect(arena, child, tag, out);
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}
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}
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}
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static common_peg_ast_id minimax_m3_value_of(const common_peg_ast_arena & arena, const common_peg_ast_node & node) {
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for (auto child_id : node.children) {
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const auto & tag = arena.get(child_id).tag;
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if (tag == common_chat_peg_builder::TOOL_ARG_VALUE ||
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tag == common_chat_peg_builder::TOOL_ARG_STRING_VALUE ||
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tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_OBJECT ||
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tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_ARRAY) {
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return child_id;
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}
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}
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return COMMON_PEG_INVALID_AST_ID;
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}
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static std::string minimax_m3_value_to_json(const common_peg_ast_arena & arena, common_peg_ast_id id, bool closed);
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static std::string minimax_m3_member_to_json(const common_peg_ast_arena & arena, const common_peg_ast_node & node) {
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auto name_id = arena.find_by_tag(node, common_chat_peg_builder::TOOL_ARG_NAME);
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if (name_id == COMMON_PEG_INVALID_AST_ID) {
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return "";
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}
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return ordered_json(arena.get(name_id).text).dump() + ":" +
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minimax_m3_value_to_json(arena, minimax_m3_value_of(arena, node), !node.is_partial);
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}
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static std::string minimax_m3_container_to_json(const common_peg_ast_arena & arena,
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const common_peg_ast_node & node,
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bool is_object,
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bool closed) {
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const std::string tag = is_object ? common_chat_peg_builder::TOOL_ARG
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: common_chat_peg_minimax_m3_mapper::TOOL_ARG_ITEM;
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std::vector<common_peg_ast_id> entries;
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minimax_m3_collect(arena, node, tag, entries);
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std::string result = is_object ? "{" : "[";
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bool add_comma = false;
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for (auto entry_id : entries) {
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const auto & entry = arena.get(entry_id);
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std::string text;
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if (is_object) {
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text = minimax_m3_member_to_json(arena, entry);
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} else {
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text = minimax_m3_value_to_json(arena, minimax_m3_value_of(arena, entry), !entry.is_partial);
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}
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if (text.empty()) {
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continue;
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}
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if (add_comma) {
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result += ",";
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}
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add_comma = true;
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result += text;
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}
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if (closed) {
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result += is_object ? "}" : "]";
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}
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return result;
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}
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static std::string minimax_m3_value_to_json(const common_peg_ast_arena & arena, common_peg_ast_id id, bool closed) {
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if (id == COMMON_PEG_INVALID_AST_ID) {
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return "";
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}
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const auto & node = arena.get(id);
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if (node.tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_OBJECT) {
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return minimax_m3_container_to_json(arena, node, /* is_object = */ true, closed);
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}
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if (node.tag == common_chat_peg_minimax_m3_mapper::TOOL_ARG_ARRAY) {
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return minimax_m3_container_to_json(arena, node, /* is_object = */ false, closed);
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}
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if (node.tag == common_chat_peg_builder::TOOL_ARG_STRING_VALUE) {
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return "\"" + escape_json_string_inner(std::string(node.text)) + (closed ? "\"" : "");
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}
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// Numbers and booleans are written verbatim by the template
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return std::string(node.text);
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}
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void common_chat_peg_minimax_m3_mapper::from_ast(const common_peg_ast_arena & arena,
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const common_peg_parse_result & result) {
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for (const auto & node : result.nodes) {
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visit(arena, node);
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}
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}
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void common_chat_peg_minimax_m3_mapper::visit(const common_peg_ast_arena & arena, common_peg_ast_id id) {
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const auto & node = arena.get(id);
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if (node.tag == common_chat_peg_builder::REASONING) {
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result.reasoning_content += std::string(node.text);
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return;
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}
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if (node.tag == common_chat_peg_builder::CONTENT) {
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result.content += std::string(node.text);
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return;
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}
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if (node.tag == common_chat_peg_builder::TOOL) {
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auto name_id = arena.find_by_tag(node, common_chat_peg_builder::TOOL_NAME);
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if (name_id != COMMON_PEG_INVALID_AST_ID) {
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common_chat_tool_call call;
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call.name = std::string(arena.get(name_id).text);
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call.arguments = minimax_m3_container_to_json(arena, node, /* is_object = */ true, !node.is_partial);
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result.tool_calls.push_back(call);
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}
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return;
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}
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for (auto child_id : node.children) {
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visit(arena, child_id);
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}
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}
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@ -40,6 +40,18 @@ class common_chat_peg_gemma4_mapper : public common_chat_peg_mapper {
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void visit(const common_peg_ast_arena & arena, common_peg_ast_id id);
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};
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class common_chat_peg_minimax_m3_mapper : public common_chat_peg_mapper {
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public:
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static constexpr const char * TOOL_ARG_OBJECT = "tool-arg-object";
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static constexpr const char * TOOL_ARG_ARRAY = "tool-arg-array";
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static constexpr const char * TOOL_ARG_ITEM = "tool-arg-item";
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common_chat_peg_minimax_m3_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
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virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result);
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private:
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void visit(const common_peg_ast_arena & arena, common_peg_ast_id id);
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};
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struct content_structure;
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struct tool_call_structure;
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273
common/chat.cpp
273
common/chat.cpp
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@ -830,6 +830,8 @@ const char * common_chat_format_name(common_chat_format format) {
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return "peg-native";
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case COMMON_CHAT_FORMAT_PEG_GEMMA4:
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return "peg-gemma4";
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case COMMON_CHAT_FORMAT_PEG_MINIMAX_M3:
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return "peg-minimax-m3";
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default:
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throw std::runtime_error("Unknown chat format");
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}
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@ -2284,6 +2286,264 @@ static common_chat_params common_chat_params_init_cohere2moe(const common_chat_t
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return data;
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}
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static common_chat_params common_chat_params_init_minimax_m3(const common_chat_template & tmpl,
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const autoparser::generation_params & inputs) {
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common_chat_params data;
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data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs);
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data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
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data.format = COMMON_CHAT_FORMAT_PEG_MINIMAX_M3;
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data.supports_thinking = true;
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data.thinking_start_tag = "<mm:think>";
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data.thinking_end_tags = {"</mm:think>"};
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// M3 prefixes every tool tag with the namespace token "]<]minimax[>[";
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// params use the parameter name as the tag (<file_path>...</file_path>).
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const std::string NS = "]<]minimax[>[";
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const std::string THINK_START = "<mm:think>";
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const std::string THINK_END = "</mm:think>";
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const std::string FC_START = NS + "<tool_call>";
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const std::string FC_END = NS + "</tool_call>";
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const std::string INVOKE_END = NS + "</invoke>";
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data.preserved_tokens = {
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NS,
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"<tool_call>",
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"</tool_call>",
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THINK_START,
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THINK_END,
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};
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|
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data.message_delimiters = {
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{ COMMON_CHAT_ROLE_ASSISTANT, "]~b]ai" },
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{ COMMON_CHAT_ROLE_USER, "]~b]user" },
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{ COMMON_CHAT_ROLE_TOOL, "]~b]tool" },
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{ COMMON_CHAT_ROLE_SYSTEM, "]~b]developer" },
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{ COMMON_CHAT_ROLE_SYSTEM, "]~b]system" },
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};
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|
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auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
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auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
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auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
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auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
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|
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const std::string GEN_PROMPT = data.generation_prompt;
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|
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using mm3 = common_chat_peg_minimax_m3_mapper;
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|
||||
if (inputs.has_continuation()) {
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const auto & msg = inputs.continue_msg;
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|
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data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
|
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if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
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data.generation_prompt += THINK_END + msg.render_content();
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||||
}
|
||||
|
||||
data.prompt += data.generation_prompt;
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||||
}
|
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|
||||
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
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auto generation_prompt = p.prefix(GEN_PROMPT, THINK_START);
|
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auto end = p.end();
|
||||
|
||||
auto reasoning = p.eps();
|
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if (extract_reasoning) {
|
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auto block = inputs.enable_thinking
|
||||
? p.literal(THINK_START) + p.space() +
|
||||
p.ac(p.reasoning(p.until(THINK_END)) + p.literal(THINK_END), THINK_END)
|
||||
: p.literal(THINK_START) + p.ac(p.until(THINK_END) + p.literal(THINK_END), THINK_END);
|
||||
|
||||
// A turn without reasoning is prefixed with a bare </mm:think>, written either by the
|
||||
// generation prompt (thinking_mode = "disabled") or by the model itself.
|
||||
reasoning = p.optional(p.choice({ block, p.literal(THINK_END) }));
|
||||
}
|
||||
|
||||
if (has_response_format) {
|
||||
auto response_format = p.rule("response-format",
|
||||
p.literal("```json") + p.space() +
|
||||
p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
|
||||
p.space() + p.literal("```"));
|
||||
return generation_prompt + reasoning + response_format + end;
|
||||
}
|
||||
|
||||
if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
|
||||
return generation_prompt + reasoning + p.content(p.rest()) + end;
|
||||
}
|
||||
|
||||
auto alternatives_of = [](const json & schema) -> std::optional<json> {
|
||||
for (const auto * keyword : { "oneOf", "anyOf" }) {
|
||||
if (schema.contains(keyword) && schema.at(keyword).is_array() && !schema.at(keyword).empty()) {
|
||||
return schema.at(keyword);
|
||||
}
|
||||
}
|
||||
return std::nullopt;
|
||||
};
|
||||
|
||||
auto tool_choice = p.choice();
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool.at("function");
|
||||
std::string name = function.at("name");
|
||||
auto params = function.contains("parameters") ? function.at("parameters") : json::object();
|
||||
|
||||
auto schema_info = common_schema_info();
|
||||
schema_info.resolve_refs(params);
|
||||
|
||||
// The template expands argument values recursively in XML (see the to_xml() macro)
|
||||
std::function<common_peg_parser(const json &, const std::string &, const std::string &)> value_of;
|
||||
std::function<common_peg_parser(const json &, const std::string &)> members_of;
|
||||
|
||||
auto element_of = [&](const std::string & tag, const json & schema, const std::string & rule_name) {
|
||||
const std::string close = NS + "</" + tag + ">";
|
||||
return p.rule(rule_name,
|
||||
p.tool_arg(
|
||||
p.tool_arg_open(
|
||||
p.literal(NS + "<") +
|
||||
p.tool_arg_name(p.literal(tag)) +
|
||||
p.literal(">")) +
|
||||
value_of(schema, rule_name, close)));
|
||||
};
|
||||
|
||||
value_of = [&](const json & schema,
|
||||
const std::string & rule_name,
|
||||
const std::string & close) -> common_peg_parser {
|
||||
auto close_tag = p.tool_arg_close(p.literal(close));
|
||||
|
||||
// A string accepts anything, so a union with a string alternative is a string
|
||||
if (schema_info.resolves_to_string(schema)) {
|
||||
return p.ac(p.tool_arg_string_value(p.until(close)) + close_tag, close);
|
||||
}
|
||||
|
||||
if (auto alternatives = alternatives_of(schema)) {
|
||||
std::vector<common_peg_parser> choices;
|
||||
|
||||
size_t index = 0;
|
||||
for (const auto & alternative : *alternatives) {
|
||||
const std::string alt_name = rule_name + "-" + std::to_string(index++);
|
||||
|
||||
// There is a risk that this breaks streaming deltas, but that's a risk we
|
||||
// assume to provide tool arg streaming.
|
||||
choices.push_back(value_of(alternative, alt_name, close));
|
||||
}
|
||||
|
||||
return p.choice(choices);
|
||||
}
|
||||
|
||||
const std::string type = schema.contains("type") && schema.at("type").is_string()
|
||||
? schema.at("type").get<std::string>()
|
||||
: "";
|
||||
|
||||
if (type == "object" && schema.contains("properties")) {
|
||||
return p.tag(mm3::TOOL_ARG_OBJECT, members_of(schema, rule_name)) + p.space() + close_tag;
|
||||
}
|
||||
|
||||
if (type == "array" && schema.contains("items")) {
|
||||
const std::string item_close = NS + "</item>";
|
||||
auto item = p.rule(rule_name + "-item",
|
||||
p.tag(mm3::TOOL_ARG_ITEM,
|
||||
p.literal(NS + "<item>") +
|
||||
value_of(schema.at("items"), rule_name + "-item", item_close)));
|
||||
return p.tag(mm3::TOOL_ARG_ARRAY, p.repeat(p.space() + item, 0, -1)) + p.space() + close_tag;
|
||||
}
|
||||
|
||||
return p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", schema, false)) + close_tag;
|
||||
};
|
||||
|
||||
// Required properties in schema order, then any number of optional ones in any order.
|
||||
members_of = [&](const json & schema, const std::string & rule_prefix) -> common_peg_parser {
|
||||
const auto & props = schema.at("properties");
|
||||
|
||||
std::set<std::string> required;
|
||||
if (schema.contains("required")) {
|
||||
schema.at("required").get_to(required);
|
||||
}
|
||||
|
||||
std::vector<common_peg_parser> required_elements;
|
||||
std::vector<common_peg_parser> optional_elements;
|
||||
for (const auto & [key, key_schema] : props.items()) {
|
||||
auto element = element_of(key, key_schema, rule_prefix + "-" + key);
|
||||
if (required.find(key) != required.end()) {
|
||||
required_elements.push_back(element);
|
||||
} else {
|
||||
optional_elements.push_back(element);
|
||||
}
|
||||
}
|
||||
|
||||
common_peg_parser members = p.eps();
|
||||
for (size_t i = 0; i < required_elements.size(); i++) {
|
||||
if (i > 0) {
|
||||
members = members + p.space();
|
||||
}
|
||||
members = members + required_elements[i];
|
||||
}
|
||||
|
||||
if (!optional_elements.empty()) {
|
||||
common_peg_parser any_optional = p.choice();
|
||||
for (const auto & element : optional_elements) {
|
||||
any_optional |= element;
|
||||
}
|
||||
members = members + p.repeat(p.space() + any_optional, 0, -1);
|
||||
}
|
||||
|
||||
return members;
|
||||
};
|
||||
|
||||
common_peg_parser invoke_body =
|
||||
params.contains("properties") ? members_of(params, "tool-" + name + "-arg") : p.eps();
|
||||
|
||||
auto func_parser = p.tool(
|
||||
p.tool_open(p.literal(NS + "<invoke name=\"") +
|
||||
p.tool_name(p.literal(name)) + p.literal("\">")) +
|
||||
p.space() + invoke_body + p.space() +
|
||||
p.tool_close(p.literal(INVOKE_END)));
|
||||
|
||||
tool_choice |= p.rule("tool-" + name, func_parser);
|
||||
});
|
||||
|
||||
auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
|
||||
|
||||
common_peg_parser tool_calls = p.eps();
|
||||
if (inputs.parallel_tool_calls) {
|
||||
tool_calls = p.trigger_rule("tool-call",
|
||||
p.literal(FC_START) + p.space() + tool_choice +
|
||||
p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
|
||||
} else {
|
||||
tool_calls = p.trigger_rule("tool-call",
|
||||
p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
|
||||
}
|
||||
|
||||
if (!require_tools) {
|
||||
tool_calls = p.optional(tool_calls);
|
||||
}
|
||||
|
||||
auto content_before_tools = p.content(p.until(FC_START));
|
||||
return generation_prompt + reasoning + content_before_tools + tool_calls + end;
|
||||
});
|
||||
|
||||
data.parser = parser.save();
|
||||
|
||||
if (include_grammar) {
|
||||
data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
foreach_function(inputs.tools, [&](const json & tool) {
|
||||
const auto & function = tool.at("function");
|
||||
auto schema = function.contains("parameters") ? function.at("parameters") : json::object();
|
||||
builder.resolve_refs(schema);
|
||||
});
|
||||
if (has_response_format) {
|
||||
auto schema = inputs.json_schema;
|
||||
builder.resolve_refs(schema);
|
||||
}
|
||||
parser.build_grammar(builder, data.grammar_lazy);
|
||||
});
|
||||
|
||||
data.grammar_triggers = {
|
||||
{ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
|
||||
};
|
||||
}
|
||||
|
||||
return data;
|
||||
}
|
||||
|
||||
namespace workaround {
|
||||
|
||||
static void map_developer_role_to_system(json & messages) {
|
||||
|
|
@ -2721,6 +2981,15 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
|
|||
return common_chat_params_init_gigachat_v3(tmpl, params);
|
||||
}
|
||||
|
||||
// MiniMax-M3: the namespace token "]<]minimax[>[" collides with the autoparser's
|
||||
// markup delimiters, so detect the template and use a dedicated parser.
|
||||
if (src.find("]<]minimax[>[") != std::string::npos &&
|
||||
src.find("<tool_call>") != std::string::npos &&
|
||||
src.find("<invoke name=") != std::string::npos) {
|
||||
LOG_DBG("Using specialized template: MiniMax-M3\n");
|
||||
return common_chat_params_init_minimax_m3(tmpl, params);
|
||||
}
|
||||
|
||||
// DeepSeek V3.2/V4 format detection: template defines dsml_token and uses it for tool calls.
|
||||
// The template source contains the token as a variable assignment, not as a literal in markup.
|
||||
// V3.2 names the tool call block "function_calls", V4 names it "tool_calls".
|
||||
|
|
@ -3012,6 +3281,8 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena & src_pars
|
|||
std::unique_ptr<common_chat_peg_mapper> mapper;
|
||||
if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
|
||||
mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
|
||||
} else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) {
|
||||
mapper = std::make_unique<common_chat_peg_minimax_m3_mapper>(msg);
|
||||
} else {
|
||||
mapper = std::make_unique<common_chat_peg_mapper>(msg);
|
||||
}
|
||||
|
|
@ -3034,6 +3305,8 @@ common_chat_msg common_chat_peg_parse(const common_peg_arena & src_pars
|
|||
std::unique_ptr<common_chat_peg_mapper> mapper;
|
||||
if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
|
||||
mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
|
||||
} else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) {
|
||||
mapper = std::make_unique<common_chat_peg_minimax_m3_mapper>(msg);
|
||||
} else {
|
||||
mapper = std::make_unique<common_chat_peg_mapper>(msg);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -233,6 +233,7 @@ enum common_chat_format {
|
|||
COMMON_CHAT_FORMAT_PEG_SIMPLE,
|
||||
COMMON_CHAT_FORMAT_PEG_NATIVE,
|
||||
COMMON_CHAT_FORMAT_PEG_GEMMA4,
|
||||
COMMON_CHAT_FORMAT_PEG_MINIMAX_M3,
|
||||
|
||||
COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1524,23 +1524,49 @@ done:
|
|||
return res;
|
||||
}
|
||||
|
||||
void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
||||
static void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
||||
auto * mem = llama_get_memory(ctx);
|
||||
if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) {
|
||||
GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str());
|
||||
}
|
||||
}
|
||||
|
||||
void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
||||
static void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
||||
auto * mem = llama_get_memory(ctx);
|
||||
llama_memory_seq_cp(mem, seq_id_src, seq_id_dst, p0, p1);
|
||||
}
|
||||
|
||||
void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
|
||||
static void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
|
||||
auto * mem = llama_get_memory(ctx);
|
||||
llama_memory_seq_add(mem, seq_id, p0, p1, delta);
|
||||
}
|
||||
|
||||
void common_memory::init(llama_context * ctx_tgt, llama_context * ctx_dft) {
|
||||
this->ctx_tgt = ctx_tgt;
|
||||
this->ctx_dft = ctx_dft;
|
||||
}
|
||||
|
||||
void common_memory::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) const {
|
||||
common_context_seq_rm(ctx_tgt, seq_id, p0, p1);
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm(ctx_dft, seq_id, p0, p1);
|
||||
}
|
||||
}
|
||||
|
||||
void common_memory::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) const {
|
||||
common_context_seq_cp(ctx_tgt, seq_id_src, seq_id_dst, p0, p1);
|
||||
if (ctx_dft) {
|
||||
common_context_seq_cp(ctx_dft, seq_id_src, seq_id_dst, p0, p1);
|
||||
}
|
||||
}
|
||||
|
||||
void common_memory::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) const {
|
||||
common_context_seq_add(ctx_tgt, seq_id, p0, p1, delta);
|
||||
if (ctx_dft) {
|
||||
common_context_seq_add(ctx_dft, seq_id, p0, p1, delta);
|
||||
}
|
||||
}
|
||||
|
||||
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) {
|
||||
std::vector<llama_adapter_lora *> loras;
|
||||
std::vector<float> scales;
|
||||
|
|
|
|||
|
|
@ -174,6 +174,7 @@ enum common_speculative_type {
|
|||
COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, // Eagle3 speculative decoding
|
||||
COMMON_SPECULATIVE_TYPE_DRAFT_MTP, // Multi-token prediction
|
||||
COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, // DFlash speculative decoding
|
||||
COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, // DSpark speculative decoding (DFlash + Markov head)
|
||||
COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, // simple self-speculative decoding based on n-grams
|
||||
COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, // self-speculative decoding with n-gram keys only
|
||||
COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
|
||||
|
|
@ -389,7 +390,7 @@ struct common_params_speculative {
|
|||
|
||||
uint32_t need_n_rs_seq() const {
|
||||
bool needs_rs_seq = std::any_of(types.begin(), types.end(), [&](auto t) {
|
||||
return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH;
|
||||
return t == COMMON_SPECULATIVE_TYPE_DRAFT_MTP || t == COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3 || t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
|
||||
});
|
||||
|
||||
return needs_rs_seq ? draft.n_max : 0u;
|
||||
|
|
@ -949,10 +950,17 @@ enum common_context_seq_rm_type {
|
|||
// note: clears the memory of the context
|
||||
common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx);
|
||||
|
||||
// aborts execution on failure
|
||||
void common_context_seq_rm (llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1);
|
||||
void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta);
|
||||
void common_context_seq_cp (llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1);
|
||||
struct common_memory {
|
||||
llama_context * ctx_tgt = nullptr;
|
||||
llama_context * ctx_dft = nullptr;
|
||||
|
||||
void init(llama_context * ctx_tgt, llama_context * ctx_dft = nullptr);
|
||||
|
||||
// aborts execution on failure
|
||||
void seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) const;
|
||||
void seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) const;
|
||||
void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) const;
|
||||
};
|
||||
|
||||
//
|
||||
// Batch utils
|
||||
|
|
|
|||
|
|
@ -34,6 +34,7 @@ const std::map<std::string, common_speculative_type> common_speculative_type_fro
|
|||
{"draft-eagle3", COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3},
|
||||
{"draft-mtp", COMMON_SPECULATIVE_TYPE_DRAFT_MTP},
|
||||
{"draft-dflash", COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH},
|
||||
{"draft-dspark", COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK},
|
||||
{"ngram-simple", COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE},
|
||||
{"ngram-map-k", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K},
|
||||
{"ngram-map-k4v", COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V},
|
||||
|
|
@ -437,6 +438,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
|
|||
int32_t n_embd_dec = 0; // draft hidden size
|
||||
int32_t n_embd_enc = 0; // target_layer_ids_n * target_hidden_size
|
||||
int32_t n_embd_tgt = 0; // target model hidden size
|
||||
int32_t n_layer_tgt = 0; // target model layer count
|
||||
|
||||
const int32_t * target_layer_ids = nullptr; // model_dft's extract layer indices
|
||||
uint32_t target_layer_ids_n = 0;
|
||||
|
|
@ -478,6 +480,7 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
|
|||
n_embd_tgt = llama_model_n_embd(model_tgt);
|
||||
n_embd_dec = llama_model_n_embd(model_dft);
|
||||
n_embd_enc = (int32_t) target_layer_ids_n * n_embd_tgt;
|
||||
n_layer_tgt = llama_model_n_layer(model_tgt);
|
||||
|
||||
const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
|
||||
batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd_dec, /*n_seq_max=*/ 1);
|
||||
|
|
@ -510,9 +513,15 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
|
|||
}
|
||||
}
|
||||
|
||||
// turn on extraction of the target layers' input embeddings
|
||||
// turn on extraction of the target layers' hidden states
|
||||
for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
|
||||
llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
|
||||
if (target_layer_ids[k] < n_layer_tgt) {
|
||||
llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
|
||||
} else if (target_layer_ids[k] == n_layer_tgt) {
|
||||
llama_set_embeddings_nextn(ctx_tgt, true, /*masked*/ false);
|
||||
} else {
|
||||
GGML_ABORT("EAGLE3: target layer id %d exceeds target n_layer %d", target_layer_ids[k], n_layer_tgt);
|
||||
}
|
||||
}
|
||||
|
||||
// turn on extraction of the draft model's pre-norm hidden state
|
||||
|
|
@ -600,7 +609,9 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
|
|||
features_buf.resize((size_t) n_tokens * n_embd_enc, 0.0f);
|
||||
|
||||
for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
|
||||
const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
|
||||
const float * layer = target_layer_ids[k] < n_layer_tgt
|
||||
? llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k])
|
||||
: llama_get_embeddings_nextn(ctx_tgt);
|
||||
if (!layer) {
|
||||
GGML_ABORT("EAGLE3: target layer %d input not extracted.", target_layer_ids[k]);
|
||||
}
|
||||
|
|
@ -918,15 +929,20 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
|
|||
int32_t block_size = 0;
|
||||
llama_token mask_token_id = 0;
|
||||
|
||||
// draft-dspark: the draft carries a Markov head and uses an anchor-first block layout
|
||||
const bool is_dspark;
|
||||
|
||||
const int32_t * target_layer_ids = nullptr; // model_dft's extract layer indices
|
||||
uint32_t target_layer_ids_n = 0;
|
||||
|
||||
// scratch buffer for concatenated target features [n_tokens, n_embd_enc]
|
||||
std::vector<float> features_buf;
|
||||
|
||||
common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq)
|
||||
: common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, n_seq)
|
||||
common_speculative_impl_draft_dflash(const common_params_speculative & params, uint32_t n_seq,
|
||||
common_speculative_type type = COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)
|
||||
: common_speculative_impl(type, n_seq)
|
||||
, params(params.draft)
|
||||
, is_dspark(type == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)
|
||||
{
|
||||
auto * ctx_tgt = this->params.ctx_tgt;
|
||||
auto * ctx_dft = this->params.ctx_dft;
|
||||
|
|
@ -953,16 +969,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
|
|||
}
|
||||
mask_token_id = llama_vocab_mask(llama_model_get_vocab(model_dft));
|
||||
|
||||
LOG_INF("%s: adding speculative implementation 'draft-dflash'\n", __func__);
|
||||
LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(type).c_str());
|
||||
LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
|
||||
LOG_INF("%s: - block_size=%d, mask_token_id=%d, n_extract=%u\n", __func__, block_size, mask_token_id, target_layer_ids_n);
|
||||
|
||||
// DFlash input is [id_last, <mask> * (block_size-1)], so it can draft at most block_size-1 tokens per step
|
||||
if (this->params.n_max > block_size - 1 || this->params.n_min > block_size - 1) {
|
||||
LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained DFlash block size %d -- clamping to %d\n",
|
||||
__func__, this->params.n_max, this->params.n_min, block_size, block_size - 1);
|
||||
this->params.n_max = std::min(this->params.n_max, block_size - 1);
|
||||
this->params.n_min = std::min(this->params.n_min, block_size - 1);
|
||||
// DFlash input is [id_last, <mask> * (block_size-1)]: in-place denoising yields at most
|
||||
// block_size-1 draft tokens, DSpark yield a full block_size draft tokens
|
||||
const int32_t n_draft_max = is_dspark ? block_size : block_size - 1;
|
||||
if (this->params.n_max > n_draft_max || this->params.n_min > n_draft_max) {
|
||||
LOG_WRN("%s: requested draft size (n_max=%d, n_min=%d) exceeds the trained block size %d -- clamping to %d\n",
|
||||
__func__, this->params.n_max, this->params.n_min, block_size, n_draft_max);
|
||||
this->params.n_max = std::min(this->params.n_max, n_draft_max);
|
||||
this->params.n_min = std::min(this->params.n_min, n_draft_max);
|
||||
}
|
||||
|
||||
batch = llama_batch_init(llama_n_batch(ctx_dft), 0, n_seq);
|
||||
|
|
@ -1126,12 +1144,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
|
|||
|
||||
const int32_t n = (int32_t) dp.n_past;
|
||||
|
||||
int32_t n_draft = params.n_max;
|
||||
if (dp.n_max > 0) {
|
||||
n_draft = std::min(n_draft, dp.n_max);
|
||||
}
|
||||
const int32_t n_draft = params.n_max;
|
||||
|
||||
const int32_t n_block_tokens = n_draft + 1; // id_last + n_draft * <mask>
|
||||
const int32_t n_block_tokens = n_draft + (is_dspark ? 0 : 1);
|
||||
i_block_beg[seq_id] = batch.n_tokens;
|
||||
n_block [seq_id] = n_block_tokens;
|
||||
for (int32_t i = 0; i < n_block_tokens; ++i) {
|
||||
|
|
@ -1163,27 +1178,57 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
|
|||
|
||||
auto & result = *dp.result;
|
||||
|
||||
// greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1
|
||||
for (int32_t i = 1; i < n_block_tokens; ++i) {
|
||||
common_sampler_sample(smpl, ctx_dft, beg + i, true);
|
||||
if (is_dspark) {
|
||||
// DSpark predicts the next token from position 0 and optionally truncates
|
||||
// at the first position below the confidence threshold.
|
||||
const float * conf = params.p_min > 0.0f ? llama_get_embeddings_nextn(ctx_dft) : nullptr;
|
||||
|
||||
const auto * cur_p = common_sampler_get_candidates(smpl, true);
|
||||
for (int32_t i = 0; i < n_block_tokens; ++i) {
|
||||
const int32_t idx = beg + i;
|
||||
|
||||
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
|
||||
LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
|
||||
seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p,
|
||||
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
|
||||
if (conf && conf[(size_t) idx * n_embd_dec] < params.p_min) {
|
||||
break;
|
||||
}
|
||||
|
||||
common_sampler_sample(smpl, ctx_dft, idx, true);
|
||||
|
||||
const auto * cur_p = common_sampler_get_candidates(smpl, true);
|
||||
|
||||
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
|
||||
LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
|
||||
seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
|
||||
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
|
||||
}
|
||||
|
||||
const llama_token id = cur_p->data[0].id;
|
||||
|
||||
common_sampler_accept(smpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
}
|
||||
} else {
|
||||
// greedily read the predicted block at this sequence's noise positions 1..n_block_tokens-1
|
||||
for (int32_t i = 1; i < n_block_tokens; ++i) {
|
||||
common_sampler_sample(smpl, ctx_dft, beg + i, true);
|
||||
|
||||
const llama_token id = cur_p->data[0].id;
|
||||
const auto * cur_p = common_sampler_get_candidates(smpl, true);
|
||||
|
||||
if (cur_p->data[0].p < params.p_min) {
|
||||
break;
|
||||
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
|
||||
LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
|
||||
seq_id, k, i - 1, cur_p->data[k].id, cur_p->data[k].p,
|
||||
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
|
||||
}
|
||||
|
||||
const llama_token id = cur_p->data[0].id;
|
||||
|
||||
if (cur_p->data[0].p < params.p_min) {
|
||||
break;
|
||||
}
|
||||
|
||||
common_sampler_accept(smpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
}
|
||||
|
||||
common_sampler_accept(smpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
}
|
||||
|
||||
if (result.size() < (size_t) params.n_min) {
|
||||
|
|
@ -2145,6 +2190,7 @@ std::string common_speculative_type_to_str(common_speculative_type type) {
|
|||
case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3: return "draft-eagle3";
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_MTP: return "draft-mtp";
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH: return "draft-dflash";
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: return "draft-dspark";
|
||||
case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: return "ngram-simple";
|
||||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K: return "ngram-map-k";
|
||||
case COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V: return "ngram-map-k4v";
|
||||
|
|
@ -2198,6 +2244,7 @@ int32_t common_speculative_n_max(const common_params_speculative * spec) {
|
|||
case COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3:
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_MTP:
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH:
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK:
|
||||
n_max = std::max(n_max, std::max(0, spec->draft.n_max));
|
||||
break;
|
||||
case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE:
|
||||
|
|
@ -2342,6 +2389,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
|
|||
bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
|
||||
bool has_draft_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
|
||||
bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr;
|
||||
bool has_draft_dspark = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)) && params.draft.ctx_dft != nullptr;
|
||||
|
||||
|
||||
|
||||
|
|
@ -2352,7 +2400,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
|
|||
bool has_ngram_mod = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
|
||||
|
||||
// when adding a new type - update here the logic above
|
||||
static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 10);
|
||||
static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 11);
|
||||
|
||||
// this list here defines the priority of the speculators
|
||||
// the one with highest priority are listed first
|
||||
|
|
@ -2385,6 +2433,9 @@ common_speculative * common_speculative_init(common_params_speculative & params,
|
|||
if (has_draft_dflash) {
|
||||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params));
|
||||
}
|
||||
if (has_draft_dspark) {
|
||||
configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params));
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
|
||||
|
|
@ -2409,6 +2460,11 @@ common_speculative * common_speculative_init(common_params_speculative & params,
|
|||
impls.push_back(std::make_unique<common_speculative_impl_draft_dflash>(config.params, n_seq));
|
||||
break;
|
||||
}
|
||||
case COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK: {
|
||||
impls.push_back(std::make_unique<common_speculative_impl_draft_dflash>(
|
||||
config.params, n_seq, COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK));
|
||||
break;
|
||||
}
|
||||
case COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE: {
|
||||
common_ngram_map ngram_map = get_common_ngram_map(config.type, config.params.ngram_simple);
|
||||
|
||||
|
|
|
|||
|
|
@ -53,6 +53,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
|
|||
"DeepseekV3ForCausalLM": "deepseek",
|
||||
"DeepseekV32ForCausalLM": "deepseek",
|
||||
"DFlashDraftModel": "qwen",
|
||||
"Qwen3DSparkModel": "qwen",
|
||||
"DeepseekV4ForCausalLM": "deepseek",
|
||||
"DistilBertForMaskedLM": "bert",
|
||||
"DistilBertForSequenceClassification": "bert",
|
||||
|
|
@ -167,6 +168,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
|
|||
"ModernBertForMaskedLM": "bert",
|
||||
"ModernBertForSequenceClassification": "bert",
|
||||
"ModernBertModel": "bert",
|
||||
"NanbeigeForCausalLM": "nanbeige",
|
||||
"NemotronForCausalLM": "nemotron",
|
||||
"NemotronHForCausalLM": "nemotron",
|
||||
"NeoBERT": "bert",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from typing import Iterable, TYPE_CHECKING
|
||||
import re
|
||||
|
||||
from typing import Callable, Iterable, TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
|
|
@ -213,12 +215,47 @@ class Glm4MoeLiteModel(DeepseekV2Model):
|
|||
class GlmMoeDsaModel(DeepseekV2Model):
|
||||
model_arch = gguf.MODEL_ARCH.GLM_DSA
|
||||
skip_mtp = False
|
||||
supports_mtp_export = True
|
||||
|
||||
# Trunk layer count, stashed before indexing so the classmethod
|
||||
# filter_tensors can identify the appended NextN/MTP block (mirrors
|
||||
# HYV3Model / Step35Model).
|
||||
_n_main_layers: int | None = None
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
|
||||
self.block_count = self.hparams["num_hidden_layers"]
|
||||
if not self.no_mtp:
|
||||
self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
|
||||
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
|
||||
|
||||
def index_tensors(self, remote_hf_model_id: str | None = None):
|
||||
type(self)._n_main_layers = self.hparams["num_hidden_layers"]
|
||||
return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
|
||||
|
||||
@classmethod
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
if (titem := super().filter_tensors(item)) is None:
|
||||
return None
|
||||
name, gen = titem
|
||||
|
||||
# GLM-5.2 appends the NextN/MTP block past num_hidden_layers
|
||||
# (model.layers.78 -> blk.78 in the 79-block file).
|
||||
assert cls._n_main_layers is not None
|
||||
is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
|
||||
|
||||
# --no-mtp: drop the appended NextN block entirely.
|
||||
if is_mtp and cls.no_mtp:
|
||||
return None
|
||||
# --mtp: keep ONLY NextN-block tensors plus the shared embeddings/
|
||||
# norm/lm_head (so the resulting GGUF carries just the draft head).
|
||||
if cls.mtp_only and not is_mtp and name not in (
|
||||
"model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
|
||||
):
|
||||
return None
|
||||
|
||||
return name, gen
|
||||
|
||||
def set_vocab(self):
|
||||
return self._set_vocab_glm()
|
||||
|
||||
|
|
@ -230,7 +267,7 @@ class GlmMoeDsaModel(DeepseekV2Model):
|
|||
self.gguf_writer.add_rope_dimension_count(int(rope_dim * partial_rotary_factor))
|
||||
|
||||
# NextN/MTP prediction layers
|
||||
if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
|
||||
if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
|
||||
self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
|
||||
|
||||
# DSA indexer parameters
|
||||
|
|
|
|||
|
|
@ -69,9 +69,14 @@ class LlamaModel(TextModel):
|
|||
target_config = {**target_config, **target_config["text_config"]}
|
||||
self.target_vocab_size = target_config["vocab_size"]
|
||||
|
||||
# target_layers: derived from target model layer count (low/mid/high)
|
||||
# target_layers: use the eagle3 config's explicit aux hidden-state layer ids
|
||||
# if present, else derive from the target layer count.
|
||||
target_num_layers = target_config["num_hidden_layers"]
|
||||
target_layers = [2, target_num_layers // 2, target_num_layers - 3]
|
||||
aux_layer_ids = eagle3_raw_config.get("eagle_aux_hidden_state_layer_ids")
|
||||
if aux_layer_ids:
|
||||
target_layers = aux_layer_ids
|
||||
else:
|
||||
target_layers = [2, target_num_layers // 2, target_num_layers - 3]
|
||||
logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
|
||||
self.gguf_writer.add_target_layers(target_layers)
|
||||
|
||||
|
|
@ -90,6 +95,12 @@ class LlamaModel(TextModel):
|
|||
logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
|
||||
self.gguf_writer.add_norm_before_residual(norm_before_residual)
|
||||
|
||||
# norm_before_fc: RMSNorm applied to the fused target features before the
|
||||
# fc projection (e.g. nvidia/gpt-oss-120b-Eagle3-v3)
|
||||
norm_before_fc = eagle3_raw_config.get("norm_before_fc", False)
|
||||
logger.info(f"EAGLE-3: norm_before_fc = {norm_before_fc}")
|
||||
self.gguf_writer.add_norm_before_fc(norm_before_fc)
|
||||
|
||||
def set_vocab(self):
|
||||
# eagle3: use tokenizer from target model if provided
|
||||
original_dir_model = None
|
||||
|
|
@ -222,6 +233,9 @@ class LlamaModel(TextModel):
|
|||
if name == "fc.weight":
|
||||
yield (name, data_torch)
|
||||
return
|
||||
if name == "input_norm.weight":
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ENC_OUTPUT_NORM), data_torch)
|
||||
return
|
||||
if name == "d2t":
|
||||
# store for manual int64 handling in prepare_tensors (avoid F32 conversion)
|
||||
if not hasattr(self, '_eagle3_int_tensors'):
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
|
||||
from typing import Callable, TYPE_CHECKING
|
||||
from typing import Any, Callable, Iterable, TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
|
|
@ -229,7 +230,13 @@ class MimoV2Model(TextModel):
|
|||
|
||||
|
||||
@ModelBase.register("MiMoV2ForCausalLM")
|
||||
class MiMoV2VisionModel(MmprojModel):
|
||||
class MiMoV2VisionAudioModel(MmprojModel):
|
||||
has_audio_encoder = True
|
||||
|
||||
_audio_tok_hparams: dict[str, Any] | None = None
|
||||
_rvq_codebook_sizes: list[int] | None = None
|
||||
_code_embd: dict[int, Tensor] | None = None
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
assert self.hparams_vision is not None
|
||||
|
|
@ -253,10 +260,22 @@ class MiMoV2VisionModel(MmprojModel):
|
|||
self.visual_token_window_size = int(hp.get("visual_token_window_size", -1))
|
||||
self.use_sink = bool(hp.get("use_sink", False))
|
||||
|
||||
def get_audio_config(self) -> dict[str, Any] | None:
|
||||
if self._audio_tok_hparams is None:
|
||||
path = self.dir_model / "audio_tokenizer" / "config.json"
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
cfg = json.load(f)
|
||||
# aliases so MmprojModel.find_aparam() / n_block_keys can resolve them
|
||||
cfg["hidden_size"] = cfg["d_model"]
|
||||
cfg["intermediate_size"] = cfg["encoder_ffn_dim"]
|
||||
cfg["num_attention_heads"] = cfg["encoder_attention_heads"]
|
||||
self._audio_tok_hparams = cfg
|
||||
return self._audio_tok_hparams
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
|
||||
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MIMOVL)
|
||||
self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.MIMOVL)
|
||||
self.gguf_writer.add_vision_use_silu(True)
|
||||
self.gguf_writer.add_vision_head_count_kv(self.num_kv_heads)
|
||||
self.gguf_writer.add_vision_spatial_merge_size(self.spatial_merge_size)
|
||||
|
|
@ -266,19 +285,45 @@ class MiMoV2VisionModel(MmprojModel):
|
|||
self.gguf_writer.add_vision_min_pixels(int(self.preprocessor_config["min_pixels"]))
|
||||
self.gguf_writer.add_vision_max_pixels(int(self.preprocessor_config["max_pixels"]))
|
||||
|
||||
assert self.hparams_audio is not None
|
||||
self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.MIMO_AUDIO)
|
||||
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["n_mels"])
|
||||
self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
|
||||
|
||||
assert self._rvq_codebook_sizes is not None
|
||||
self.gguf_writer.add_audio_rvq_num_quantizers(len(self._rvq_codebook_sizes))
|
||||
self.gguf_writer.add_audio_rvq_codebook_size(self._rvq_codebook_sizes)
|
||||
|
||||
n_layer = self.hparams_audio["encoder_layers"]
|
||||
swa_per_block = self.hparams_audio.get("swa_per_block", 1)
|
||||
if self.hparams_audio.get("hybrid_attention") and swa_per_block > 1:
|
||||
wa_pattern = [0 if i % swa_per_block < swa_per_block - 1 else -1 for i in range(n_layer)]
|
||||
else:
|
||||
wa_pattern = [-1] * n_layer
|
||||
self.gguf_writer.add_audio_wa_pattern_mode(wa_pattern)
|
||||
self.gguf_writer.add_audio_window_size(int(self.hparams_audio["encoder_attn_window_size"][0]))
|
||||
|
||||
audio_cfg = self.global_config["audio_config"]
|
||||
self.gguf_writer.add_audio_local_block_count(int(audio_cfg["input_local_layers"]))
|
||||
self.gguf_writer.add_audio_local_group_size(int(audio_cfg["group_size"]))
|
||||
|
||||
def tensor_force_quant(self, name, new_name, bid, n_dims):
|
||||
# Sinks must be F32: any sink-style softmax/mask add in ggml requires
|
||||
# F32, and we fold sinks into a host-built F32 mask at encode time.
|
||||
if new_name.endswith(".attn_sinks"):
|
||||
# for audio encoder: keep codebook in F32
|
||||
if new_name in (
|
||||
gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK] + ".weight",
|
||||
gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_MM_CODE_EMBD] + ".weight",
|
||||
):
|
||||
return gguf.GGMLQuantizationType.F32
|
||||
if ("encoder.conv" in name or "encoder.down_sample_layer" in name) and name.endswith(".weight"):
|
||||
return gguf.GGMLQuantizationType.F32
|
||||
return super().tensor_force_quant(name, new_name, bid, n_dims)
|
||||
|
||||
@classmethod
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
name, _ = item
|
||||
if not name.startswith("visual."):
|
||||
return None
|
||||
return super().filter_tensors(item)
|
||||
if name.startswith("visual.") or name.startswith("speech_embeddings.") or name.startswith("audio_encoder."):
|
||||
return super().filter_tensors(item)
|
||||
return None
|
||||
|
||||
def modify_tensors(self, data_torch, name, bid):
|
||||
# Conv3D patch embed: split along the temporal axis (kt=2) into two Conv2D
|
||||
|
|
@ -292,4 +337,64 @@ class MiMoV2VisionModel(MmprojModel):
|
|||
yield (embd_name + ".weight.1", data_torch[:, :, 1, ...])
|
||||
return
|
||||
|
||||
if m := re.match(r"^speech_embeddings\.(\d+)\.weight$", name):
|
||||
if self._code_embd is None:
|
||||
self._code_embd = {}
|
||||
self._code_embd[int(m.group(1))] = data_torch
|
||||
|
||||
n_channels = int(self.global_config["audio_config"]["audio_channels"])
|
||||
if len(self._code_embd) < n_channels:
|
||||
return
|
||||
merged = torch.stack([self._code_embd.pop(i) for i in range(n_channels)], dim=0)
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_CODE_EMBD), merged)
|
||||
return
|
||||
|
||||
if "conv1.bias" in name or "conv2.bias" in name:
|
||||
# transpose conv1/conv2 bias so it broadcasts against [n_frames, C_out, 1]
|
||||
data_torch = data_torch.unsqueeze(-1)
|
||||
|
||||
if name == "audio_encoder.projection.mlp.0.weight":
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1), data_torch)
|
||||
return
|
||||
if name == "audio_encoder.projection.mlp.2.weight":
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2), data_torch)
|
||||
return
|
||||
|
||||
yield from super().modify_tensors(data_torch, name, bid)
|
||||
|
||||
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
|
||||
# note: audio encoder is in its own subdir "audio_tokenizer"
|
||||
from safetensors.torch import load_file
|
||||
|
||||
tok_dir = self.dir_model / "audio_tokenizer"
|
||||
state_dict = load_file(tok_dir / "model.safetensors")
|
||||
|
||||
codebook_re = re.compile(r"^encoder\.quantizer\.vq\.layers\.(\d+)\._codebook\.embed$")
|
||||
codebooks: dict[int, Tensor] = {}
|
||||
|
||||
# EMA/training-only RVQ buffers - not needed for inference (nearest-codebook
|
||||
# lookup only reads "_codebook.embed")
|
||||
skip_suffixes = (
|
||||
"_codebook.cluster_size",
|
||||
"_codebook.embed_avg",
|
||||
"_codebook.inited",
|
||||
)
|
||||
for name, tensor in state_dict.items():
|
||||
if name.endswith(skip_suffixes):
|
||||
continue
|
||||
if m := codebook_re.match(name):
|
||||
codebooks[int(m.group(1))] = tensor
|
||||
continue
|
||||
yield name, tensor
|
||||
|
||||
# gather codebooks and merge into 3D tensor, similar to MoE MLP tensors
|
||||
n_q = len(codebooks)
|
||||
ordered = [codebooks[i] for i in range(n_q)]
|
||||
self._rvq_codebook_sizes = [int(cb.shape[0]) for cb in ordered]
|
||||
max_bins = max(self._rvq_codebook_sizes)
|
||||
dim = ordered[0].shape[1]
|
||||
merged = ordered[0].new_zeros(n_q, max_bins, dim)
|
||||
for i, cb in enumerate(ordered):
|
||||
merged[i, : cb.shape[0], :] = cb
|
||||
|
||||
yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_ENC_RVQ_CODEBOOK), merged)
|
||||
|
|
|
|||
24
conversion/nanbeige.py
Normal file
24
conversion/nanbeige.py
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from .base import ModelBase, gguf, logger
|
||||
from .llama import LlamaModel
|
||||
|
||||
|
||||
@ModelBase.register("NanbeigeForCausalLM")
|
||||
class NanbeigeModel(LlamaModel):
|
||||
model_arch = gguf.MODEL_ARCH.NANBEIGE
|
||||
undo_permute = True
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
super().set_gguf_parameters()
|
||||
hparams = self.hparams
|
||||
|
||||
n_loops = int(hparams.get("num_loops", 1) or 1)
|
||||
if n_loops < 1:
|
||||
n_loops = 1
|
||||
self.gguf_writer.add_num_loops(n_loops)
|
||||
logger.info(f"gguf: num_loops = {n_loops}")
|
||||
|
||||
skip_loop_final_norm = bool(hparams.get("skip_loop_final_norm", False))
|
||||
self.gguf_writer.add_skip_loop_final_norm(skip_loop_final_norm)
|
||||
logger.info(f"gguf: skip_loop_final_norm = {skip_loop_final_norm}")
|
||||
|
|
@ -39,28 +39,48 @@ class NemotronNanoV2VLModel(MmprojModel):
|
|||
}
|
||||
return vision_config
|
||||
|
||||
def get_audio_config(self) -> dict[str, Any] | None:
|
||||
return self.global_config.get("sound_config")
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
if "image_mean" not in self.preprocessor_config:
|
||||
self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
|
||||
if "image_std" not in self.preprocessor_config:
|
||||
self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]
|
||||
|
||||
if self.hparams_audio is not None:
|
||||
self.has_vision_encoder = True
|
||||
self.has_audio_encoder = True
|
||||
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
|
||||
self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
|
||||
self.gguf_writer.add_audio_subsampling_factor(self.hparams_audio["subsampling_factor"])
|
||||
self.gguf_writer.add_audio_conv_kernel_size(self.hparams_audio["conv_kernel_size"])
|
||||
self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.PARAKEET)
|
||||
self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
|
||||
else:
|
||||
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
|
||||
|
||||
super().set_gguf_parameters()
|
||||
hparams = self.global_config
|
||||
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
|
||||
self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
|
||||
self.gguf_writer.add_vision_use_gelu(True)
|
||||
downsample_ratio = hparams.get("downsample_ratio", 0.5)
|
||||
self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
|
||||
|
||||
def tensor_force_quant(self, name, new_name, bid, n_dims):
|
||||
if ".position_embd." in new_name or "pos_embed" in new_name:
|
||||
return gguf.GGMLQuantizationType.F32
|
||||
if "sound_encoder" in name or new_name.startswith("mm.a."):
|
||||
if "bias" in new_name or "norm" in new_name:
|
||||
return gguf.GGMLQuantizationType.F32
|
||||
if "conv" in new_name and "weight" in new_name:
|
||||
return gguf.GGMLQuantizationType.F32
|
||||
|
||||
return super().tensor_force_quant(name, new_name, bid, n_dims)
|
||||
|
||||
@classmethod
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
name, gen = item
|
||||
if (titem := super().filter_tensors(item)) is None:
|
||||
return None
|
||||
name, gen = titem
|
||||
|
||||
if "input_conditioner" in name:
|
||||
return None
|
||||
|
|
@ -69,14 +89,18 @@ class NemotronNanoV2VLModel(MmprojModel):
|
|||
if "radio_model.model.patch_generator.video_embedder" in name:
|
||||
return None
|
||||
|
||||
if not name.startswith("vision_model.radio_model.model.") and not name.startswith("mlp1."):
|
||||
if not name.startswith(("vision_model.radio_model.model.", "mlp1.", "sound_encoder.", "sound_projection.")):
|
||||
return None
|
||||
|
||||
if "patch_generator.pos_embed" in name:
|
||||
if not name.endswith(".weight"):
|
||||
name += ".weight"
|
||||
|
||||
return super().filter_tensors((name, gen))
|
||||
# num_batches is only used for training not inference.
|
||||
if "conv.norm" in name and "num_batches" in name:
|
||||
return None
|
||||
|
||||
return name, gen
|
||||
|
||||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||||
# RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
|
||||
|
|
@ -104,7 +128,26 @@ class NemotronNanoV2VLModel(MmprojModel):
|
|||
n_embd = self.hparams["hidden_size"]
|
||||
data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)
|
||||
|
||||
yield from super().modify_tensors(data_torch, name, bid)
|
||||
if "depthwise_conv.weight" in name:
|
||||
data_torch = data_torch.unsqueeze(-1)
|
||||
data_torch = data_torch.permute(3, 1, 0, 2).contiguous()
|
||||
|
||||
if "pointwise_conv" in name and name.endswith(".weight"):
|
||||
if len(data_torch.shape) == 3 and data_torch.shape[2] == 1:
|
||||
data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
|
||||
|
||||
if "subsampling.layers" in name and name.endswith(".bias"):
|
||||
if len(data_torch.shape) == 1:
|
||||
data_torch = data_torch.reshape(1, -1, 1, 1)
|
||||
|
||||
if "pointwise_conv" in name and name.endswith(".bias"):
|
||||
if len(data_torch.shape) == 1:
|
||||
data_torch = data_torch.reshape(1, -1, 1, 1)
|
||||
|
||||
for mapped_name, tensor in super().modify_tensors(data_torch, name, bid):
|
||||
if name.startswith("sound_projection.") and mapped_name.startswith("mm.model.mlp."):
|
||||
mapped_name = mapped_name.replace("mm.model.mlp.", "mm.a.mlp.")
|
||||
yield mapped_name, tensor
|
||||
|
||||
|
||||
@ModelBase.register("NemotronForCausalLM")
|
||||
|
|
|
|||
|
|
@ -688,3 +688,23 @@ class DFlashModel(Qwen3Model):
|
|||
if not name.startswith("model."):
|
||||
name = "model." + name
|
||||
return super().filter_tensors((name, gen))
|
||||
|
||||
|
||||
@ModelBase.register("Qwen3DSparkModel")
|
||||
class DSparkModel(DFlashModel):
|
||||
# DSpark = DFlash + a semi-autoregressive Markov head
|
||||
model_arch = gguf.MODEL_ARCH.DFLASH
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
# normalize the flat DeepSpec schema to DFlash's nested dflash_config
|
||||
self.hparams.setdefault("dflash_config", {
|
||||
k: self.hparams[k] for k in ("target_layer_ids", "mask_token_id") if k in self.hparams
|
||||
})
|
||||
|
||||
@classmethod
|
||||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
name, gen = item
|
||||
if name.endswith(("embed_tokens.weight", "lm_head.weight")):
|
||||
return None
|
||||
return super().filter_tensors((name, gen))
|
||||
|
|
|
|||
|
|
@ -179,12 +179,12 @@ class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
|
|||
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
|
||||
name, gen = item
|
||||
|
||||
if not name.startswith("visual.") and not name.startswith("audio_tower."):
|
||||
return None
|
||||
|
||||
if name.startswith("thinker."):
|
||||
name = name.replace("thinker.", "")
|
||||
|
||||
if not name.startswith("visual.") and not name.startswith("audio_tower."):
|
||||
return None
|
||||
|
||||
if "audio_bos_eos_token" in name:
|
||||
# this tensor is left unused in transformers code
|
||||
# https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
|
||||
|
|
|
|||
|
|
@ -6,9 +6,9 @@
|
|||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define RPC_PROTO_MAJOR_VERSION 4
|
||||
#define RPC_PROTO_MAJOR_VERSION 5
|
||||
#define RPC_PROTO_MINOR_VERSION 0
|
||||
#define RPC_PROTO_PATCH_VERSION 3
|
||||
#define RPC_PROTO_PATCH_VERSION 0
|
||||
|
||||
#ifdef __cplusplus
|
||||
static_assert(GGML_OP_COUNT == 101, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION");
|
||||
|
|
|
|||
278
ggml/src/ggml-cuda/mmq-config-rdna3-5.cuh
Normal file
278
ggml/src/ggml-cuda/mmq-config-rdna3-5.cuh
Normal file
|
|
@ -0,0 +1,278 @@
|
|||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3_5(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
||||
return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true);
|
||||
}
|
||||
278
ggml/src/ggml-cuda/mmq-config-rdna3.cuh
Normal file
278
ggml/src/ggml-cuda/mmq-config-rdna3.cuh
Normal file
|
|
@ -0,0 +1,278 @@
|
|||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna3(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
||||
return ggml_cuda_mmq_config(GGML_TYPE_COUNT, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, 256, false, true);
|
||||
}
|
||||
|
|
@ -1,77 +1,77 @@
|
|||
static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config_rdna4(ggml_type type, int J, bool fallback) {
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_1, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q8_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
|
@ -79,66 +79,62 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
|||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q2_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q2_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q3_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q4_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q5_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_Q6_K, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q6_K, MMQ_ITER_K, false, false);
|
||||
|
|
@ -146,105 +142,105 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
|||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ1_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ2_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q3_K, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_XXS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ3_S, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_XS, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_IQ4_NL, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
|
||||
|
|
@ -252,27 +248,27 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
|
|||
|
||||
// ---------------------------------------------------------------------------------------------
|
||||
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_MXFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_1, MMQ_ITER_K, false, false);
|
||||
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, true);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 16, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 32, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 48, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 128, 2, 64, 64, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 80, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 96, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
CASE(GGML_TYPE_NVFP4, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_NVFP4, MMQ_ITER_K, false, false);
|
||||
|
|
|
|||
|
|
@ -219,6 +219,8 @@ struct ggml_cuda_mmq_config {
|
|||
|
||||
#include "mmq-config-cdna.cuh"
|
||||
#include "mmq-config-rdna2.cuh"
|
||||
#include "mmq-config-rdna3.cuh"
|
||||
#include "mmq-config-rdna3-5.cuh"
|
||||
#include "mmq-config-rdna4.cuh"
|
||||
|
||||
#undef CASE
|
||||
|
|
@ -228,9 +230,15 @@ static __host__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(const ggml_type ty
|
|||
if (GGML_CUDA_CC_IS_CDNA(cc)) {
|
||||
return ggml_cuda_mmq_get_config_cdna(type, J, fallback);
|
||||
}
|
||||
if (amd_wmma_available(cc)) {
|
||||
if (GGML_CUDA_CC_IS_RDNA4(cc)) {
|
||||
return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
|
||||
}
|
||||
if (GGML_CUDA_CC_IS_RDNA3_5(cc)) {
|
||||
return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
|
||||
}
|
||||
if (GGML_CUDA_CC_IS_RDNA3(cc)) { // covers RDNA 3.0
|
||||
return ggml_cuda_mmq_get_config_rdna3(type, J, fallback);
|
||||
}
|
||||
return ggml_cuda_mmq_get_config_rdna2(type, J, fallback);
|
||||
}
|
||||
if (blackwell_mma_available(cc)) {
|
||||
|
|
@ -246,8 +254,12 @@ static constexpr __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_config(ggml_t
|
|||
#ifdef GGML_USE_HIP
|
||||
#ifdef CDNA
|
||||
return ggml_cuda_mmq_get_config_cdna(type, J, fallback);
|
||||
#elif defined(AMD_WMMA_AVAILABLE)
|
||||
#elif defined(RDNA4)
|
||||
return ggml_cuda_mmq_get_config_rdna4(type, J, fallback);
|
||||
#elif defined(RDNA3_5)
|
||||
return ggml_cuda_mmq_get_config_rdna3_5(type, J, fallback);
|
||||
#elif defined(RDNA3)
|
||||
return ggml_cuda_mmq_get_config_rdna3(type, J, fallback);
|
||||
#else
|
||||
return ggml_cuda_mmq_get_config_rdna2(type, J, fallback);
|
||||
#endif // CDNA
|
||||
|
|
|
|||
|
|
@ -9,6 +9,21 @@ using namespace cub;
|
|||
|
||||
#include "ssm-scan.cuh"
|
||||
|
||||
|
||||
// Minimum number of tokens to use SSD (State Space Duality) matmul path instead of scan path.
|
||||
// For n_tok <= this threshold, the scan kernel is used (lower overhead for short sequences).
|
||||
#define SSM_SSD_MIN_TOKENS 128
|
||||
|
||||
// prepare_dt kernel dimensions: one block per (head, seq), each block handles DT_MAX_ITEMS items.
|
||||
#define SSM_SSD_DT_BLOCK 256
|
||||
#define SSM_SSD_DT_MAX_ITEMS 32
|
||||
|
||||
// Maximum tokens the SSD path supports, derived from the prepare_dt kernel block capacity.
|
||||
#define SSM_SSD_MAX_TOKENS (SSM_SSD_DT_BLOCK * SSM_SSD_DT_MAX_ITEMS)
|
||||
|
||||
// Chunk size for chunked SSD. Caps matmul cost at O(chunk^2) per chunk.
|
||||
#define SSM_SSD_CHUNK_SIZE 256
|
||||
|
||||
// We would like to keep pragma unroll for cases where L_template is not 0,
|
||||
// so we suppress the clang transformation warning.
|
||||
#ifdef __clang__
|
||||
|
|
@ -316,6 +331,429 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
|
|||
}
|
||||
}
|
||||
|
||||
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
|
||||
// ============================================================================
|
||||
// SSD (State Space Duality) kernels for Mamba-2 prefill (n_tok > SSM_SSD_MIN_TOKENS)
|
||||
//
|
||||
// Instead of a sequential scan, SSD reformulates the output as:
|
||||
// Y = (L (.) (C @ B^T)) @ (X * dt) + decay * C @ s_init
|
||||
// where L is a causal decay mask derived from A and dt.
|
||||
//
|
||||
// This converts the O(T*N) sequential scan into parallel matmuls.
|
||||
// ============================================================================
|
||||
// Softplus(dt) and inclusive prefix sum per head using CUB BlockScan.
|
||||
// Grid: (n_head, n_seqs)
|
||||
template <int BLOCK_SIZE, int MAX_ITEMS>
|
||||
__global__ void ssm_ssd_prepare_dt_kernel(
|
||||
const float * __restrict__ dt_raw,
|
||||
float * __restrict__ dt_sp_out,
|
||||
float * __restrict__ cs_out,
|
||||
const int n_head, const int n_tok,
|
||||
const int dt_stride_tok, // elements between tokens in dt
|
||||
const int dt_stride_seq) { // elements between sequences in dt
|
||||
|
||||
const int h = blockIdx.x;
|
||||
const int s = blockIdx.y;
|
||||
|
||||
const float * dt_seq = dt_raw + s * dt_stride_seq;
|
||||
|
||||
float * dt_sp_seq = dt_sp_out + s * n_tok * n_head;
|
||||
float * cs_seq = cs_out + s * n_tok * n_head;
|
||||
|
||||
const int items_per_thread = (n_tok + BLOCK_SIZE - 1) / BLOCK_SIZE;
|
||||
|
||||
// Phase 1: softplus with interleaved distribution (t = i*BLOCK_SIZE + threadIdx.x).
|
||||
// Each warp reads BLOCK_SIZE consecutive tokens, giving coalesced dt_raw loads
|
||||
// (stride n_head between threads vs. items_per_thread*n_head in blocked layout).
|
||||
float local_vals[MAX_ITEMS];
|
||||
for (int i = 0; i < items_per_thread; i++) {
|
||||
const int t = i * BLOCK_SIZE + threadIdx.x;
|
||||
if (t < n_tok) {
|
||||
float val = dt_seq[h + t * dt_stride_tok];
|
||||
float sp = (val <= 20.0f) ? log1pf(expf(val)) : val;
|
||||
local_vals[i] = sp;
|
||||
dt_sp_seq[t * n_head + h] = sp;
|
||||
} else {
|
||||
local_vals[i] = 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
// Phase 2+3: per-step inclusive scan to build cs[] in token order.
|
||||
// With interleaved distribution the per-thread total scan would not give token-order
|
||||
// prefix sums, so we scan one BLOCK_SIZE slab at a time and carry a running total.
|
||||
#ifdef USE_CUB
|
||||
using BlockScan = cub::BlockScan<float, BLOCK_SIZE>;
|
||||
__shared__ typename BlockScan::TempStorage scan_temp;
|
||||
__shared__ float step_total;
|
||||
|
||||
float running = 0.0f;
|
||||
for (int i = 0; i < items_per_thread; i++) {
|
||||
float inclusive;
|
||||
BlockScan(scan_temp).InclusiveSum(local_vals[i], inclusive);
|
||||
const int t = i * BLOCK_SIZE + threadIdx.x;
|
||||
if (t < n_tok) {
|
||||
cs_seq[t * n_head + h] = running + inclusive;
|
||||
}
|
||||
if (threadIdx.x == BLOCK_SIZE - 1) {
|
||||
step_total = inclusive;
|
||||
}
|
||||
__syncthreads();
|
||||
running += step_total;
|
||||
}
|
||||
#else
|
||||
// Fallback: sequential prefix scan in shared memory, one slab at a time.
|
||||
__shared__ float sdata[BLOCK_SIZE];
|
||||
float running = 0.0f;
|
||||
for (int i = 0; i < items_per_thread; i++) {
|
||||
const int t = i * BLOCK_SIZE + threadIdx.x;
|
||||
sdata[threadIdx.x] = local_vals[i];
|
||||
__syncthreads();
|
||||
if (threadIdx.x == 0) {
|
||||
for (int j = 1; j < BLOCK_SIZE; j++) {
|
||||
sdata[j] += sdata[j - 1];
|
||||
}
|
||||
}
|
||||
__syncthreads();
|
||||
if (t < n_tok) {
|
||||
cs_seq[t * n_head + h] = running + sdata[threadIdx.x];
|
||||
}
|
||||
running += sdata[BLOCK_SIZE - 1];
|
||||
__syncthreads();
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
// Prepare SSD matmul inputs for one chunk: X_dt, B_weighted, C_scaled.
|
||||
// T_matmul controls precision for X_dt, B_weighted (float or half).
|
||||
// C_scaled is always float (pairs with float s_cur in step 3c).
|
||||
// Computation is always FP32; only the final store converts to T_matmul.
|
||||
// Also materializes the causal M matrix = exp(A*(cs_out - cs_in)) * CB (fused with prep to save a launch).
|
||||
// Grid: (ceil(max(C*head_dim, d_state*C, chunk_len^2) / BLOCK), n_head, n_seqs)
|
||||
template <int BLOCK_SIZE, typename T_matmul>
|
||||
__global__ void ssm_ssd_pre_matmul_kernel(
|
||||
const float * __restrict__ cs, // {n_tok, n_head} cumulative dt sums
|
||||
const float * __restrict__ dt_sp, // {n_tok, n_head} softplus(dt)
|
||||
const float * __restrict__ A, // {1, n_head}
|
||||
const float * __restrict__ x, // {head_dim, n_head, n_tok, n_seqs}
|
||||
const float * __restrict__ B, // {d_state, n_group, n_tok, n_seqs}
|
||||
const float * __restrict__ C_src, // {d_state, n_group, n_tok, n_seqs}
|
||||
T_matmul * __restrict__ X_dt, // {head_dim, C, n_head} x * dt, d-fastest
|
||||
T_matmul * __restrict__ B_weighted, // {d_state, C, n_head} B * decay_from_end
|
||||
float * __restrict__ C_scaled, // {d_state, C, n_head} C * decay_to_pos (always float)
|
||||
const float * __restrict__ CB, // {chunk_len, chunk_len, n_group, n_seqs}
|
||||
half * __restrict__ M_out, // {chunk_len, chunk_len, n_head, n_seqs}
|
||||
const int chunk_len, const int head_dim, const int n_head, const int n_group,
|
||||
const int d_state, const int A_stride,
|
||||
const int x_stride_tok, const int x_stride_seq,
|
||||
const int B_stride_tok, const int B_stride_seq,
|
||||
const int C_stride_tok, const int C_stride_seq,
|
||||
const int chunk_offset,
|
||||
const int n_tok_total) {
|
||||
|
||||
const int h = blockIdx.y;
|
||||
const int s = blockIdx.z;
|
||||
const int g = h / (n_head / n_group);
|
||||
|
||||
const float A_h = A[h * A_stride];
|
||||
const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x;
|
||||
|
||||
const int cs_seq_off = s * n_tok_total * n_head;
|
||||
const float cs_base = (chunk_offset > 0) ? cs[cs_seq_off + (chunk_offset - 1) * n_head + h] : 0.0f;
|
||||
const float cs_last = cs[cs_seq_off + (chunk_offset + chunk_len - 1) * n_head + h] - cs_base;
|
||||
|
||||
// Prepare X_dt = x * dt, stored d-fastest for coalesced reads and writes.
|
||||
const int n_xdt = chunk_len * head_dim;
|
||||
if (idx < n_xdt) {
|
||||
const int d = idx % head_dim;
|
||||
const int t = idx / head_dim;
|
||||
|
||||
const float x_val = x[s * x_stride_seq + (chunk_offset + t) * x_stride_tok + d + h * head_dim];
|
||||
const float dt_val = dt_sp[cs_seq_off + (chunk_offset + t) * n_head + h];
|
||||
|
||||
X_dt[d + t * head_dim + h * n_xdt + s * n_xdt * n_head] = (T_matmul)(x_val * dt_val);
|
||||
}
|
||||
|
||||
// Prepare B_weighted and C_scaled together: both share the same index space (d_state * chunk_len)
|
||||
// and the same cs_t load, so merging halves the cs[] global memory traffic.
|
||||
const int n_bw = d_state * chunk_len;
|
||||
if (idx < n_bw) {
|
||||
const int n = idx % d_state;
|
||||
const int t = idx / d_state;
|
||||
|
||||
const float cs_t = cs[cs_seq_off + (chunk_offset + t) * n_head + h] - cs_base;
|
||||
|
||||
const float B_val = B[s * B_stride_seq + (chunk_offset + t) * B_stride_tok + g * d_state + n];
|
||||
B_weighted[n + t * d_state + h * n_bw + s * n_bw * n_head] = (T_matmul)(B_val * __expf(A_h * (cs_last - cs_t)));
|
||||
|
||||
const float C_val = C_src[s * C_stride_seq + (chunk_offset + t) * C_stride_tok + g * d_state + n];
|
||||
C_scaled[n + t * d_state + h * n_bw + s * n_bw * n_head] = C_val * __expf(A_h * cs_t);
|
||||
}
|
||||
|
||||
// Materialize M = exp(A*(cs_out - cs_in)) * CB with causal mask.
|
||||
const int n_M = chunk_len * chunk_len;
|
||||
if (idx < n_M) {
|
||||
const int t_out = idx % chunk_len;
|
||||
const int t_in = idx / chunk_len;
|
||||
|
||||
half val;
|
||||
if (t_in <= t_out) {
|
||||
const float cs_out = cs[cs_seq_off + (chunk_offset + t_out) * n_head + h] - cs_base;
|
||||
const float cs_in = cs[cs_seq_off + (chunk_offset + t_in) * n_head + h] - cs_base;
|
||||
const float decay = __expf(A_h * (cs_out - cs_in));
|
||||
const float * CB_g = CB + (int64_t)s * chunk_len * chunk_len * n_group
|
||||
+ (int64_t)g * chunk_len * chunk_len;
|
||||
const float cb_val = CB_g[t_out + t_in * chunk_len];
|
||||
val = __float2half(decay * cb_val);
|
||||
} else {
|
||||
val = __float2half(0.0f);
|
||||
}
|
||||
|
||||
M_out[(int64_t)s * n_M * n_head + (int64_t)h * n_M + t_in * chunk_len + t_out] = val;
|
||||
}
|
||||
}
|
||||
|
||||
// Scale running state in-place: s_cur *= decay_total(chunk).
|
||||
// Called BEFORE cuBLAS state update (beta=1) to fuse inter-chunk decay.
|
||||
// Eliminates the s_old buffer and D2D memcpy vs the old approach of:
|
||||
// memcpy(s_old, s_cur) -> cuBLAS(beta=0) -> s_cur += decay * s_old
|
||||
// Grid: (ceil(d_state * head_dim / BLOCK), n_head, n_seqs)
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void ssm_ssd_scale_state_kernel(
|
||||
float * __restrict__ s_cur, // {d_state, head_dim, n_head, n_seqs}
|
||||
const float * __restrict__ cs, // {n_tok, n_head} cumulative dt sums
|
||||
const float * __restrict__ A, // {1, n_head}
|
||||
const int d_state, const int head_dim, const int n_head,
|
||||
const int chunk_offset, const int chunk_len,
|
||||
const int n_tok_total, const int A_stride) {
|
||||
|
||||
const int h = blockIdx.y;
|
||||
const int s = blockIdx.z;
|
||||
const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x;
|
||||
const int state_per_head = d_state * head_dim;
|
||||
if (idx >= state_per_head) return;
|
||||
|
||||
const float A_h = A[h * A_stride];
|
||||
const int cs_seq_off = s * n_tok_total * n_head;
|
||||
const float cs_base = (chunk_offset > 0) ? cs[cs_seq_off + (chunk_offset - 1) * n_head + h] : 0.0f;
|
||||
const float cs_last = cs[cs_seq_off + (chunk_offset + chunk_len - 1) * n_head + h] - cs_base;
|
||||
const float decay_total = __expf(A_h * cs_last);
|
||||
|
||||
const int off = s * state_per_head * n_head + h * state_per_head + idx;
|
||||
s_cur[off] *= decay_total;
|
||||
}
|
||||
|
||||
// Copy initial state from src0[ids[s]] into s_cur for each sequence.
|
||||
// Grid: (ceil(d_state * head_dim * n_head / BLOCK), n_seqs)
|
||||
template <int BLOCK_SIZE>
|
||||
__global__ void ssm_ssd_init_state_kernel(
|
||||
const float * __restrict__ src0, // {d_state, head_dim, n_head, n_rs}
|
||||
const int32_t * __restrict__ ids, // {n_seqs}
|
||||
float * __restrict__ s_cur, // {d_state, head_dim, n_head, n_seqs}
|
||||
const int state_size, // d_state * head_dim * n_head
|
||||
const int64_t s0_stride_seq) { // elements between state rows
|
||||
const int s = blockIdx.y;
|
||||
const int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x;
|
||||
if (idx >= state_size) return;
|
||||
|
||||
const float * s_src = src0 + (int64_t)ids[s] * s0_stride_seq;
|
||||
s_cur[s * state_size + idx] = s_src[idx];
|
||||
}
|
||||
|
||||
// SSD (State Space Duality) dispatch for Mamba-2 prefill.
|
||||
// Chunked matmuls: CB, materialize M + cuBLAS Y, S@C, B@X_dt.
|
||||
// All strides are in elements (floats), not bytes.
|
||||
static void ssm_scan_ssd_f32_cuda(
|
||||
ggml_backend_cuda_context & ctx,
|
||||
const float * src0_d, const float * src1_d, const float * src2_d, const float * src3_d,
|
||||
const float * src4_d, const float * src5_d, const int32_t * src6_d, float * dst_d,
|
||||
const int64_t s0_stride_seq, // state (src0) stride between seqs
|
||||
const int x_stride_tok, const int x_stride_seq, // x (src1) strides
|
||||
const int dt_stride_tok, const int dt_stride_seq, // dt (src2) strides
|
||||
const int A_stride, // A (src3) stride between heads
|
||||
const int B_stride_tok, const int B_stride_seq, // B (src4) strides
|
||||
const int C_stride_tok, const int C_stride_seq, // C (src5) strides
|
||||
const int64_t s_off, const int64_t d_state, const int64_t head_dim,
|
||||
const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq) {
|
||||
|
||||
cudaStream_t stream = ctx.stream();
|
||||
const int64_t d_inner = head_dim * n_head;
|
||||
|
||||
const int64_t chunk_size = SSM_SSD_CHUNK_SIZE;
|
||||
const int64_t n_chunks = (n_tok + chunk_size - 1) / chunk_size;
|
||||
|
||||
const int64_t state_per_head = d_state * head_dim;
|
||||
|
||||
using matmul_t = half;
|
||||
static constexpr cudaDataType_t matmul_dtype = CUDA_R_16F;
|
||||
|
||||
ggml_cuda_pool_alloc<float> dt_sp_buf(ctx.pool(), n_tok * n_head * n_seq);
|
||||
ggml_cuda_pool_alloc<float> cs_buf(ctx.pool(), n_tok * n_head * n_seq);
|
||||
ggml_cuda_pool_alloc<float> CB_buf(ctx.pool(), chunk_size * chunk_size * n_group * n_seq);
|
||||
ggml_cuda_pool_alloc<matmul_t> X_dt_buf(ctx.pool(), chunk_size * head_dim * n_head * n_seq);
|
||||
ggml_cuda_pool_alloc<matmul_t> B_w_buf(ctx.pool(), d_state * chunk_size * n_head * n_seq);
|
||||
ggml_cuda_pool_alloc<float> C_s_buf(ctx.pool(), d_state * chunk_size * n_head * n_seq);
|
||||
float * dt_sp = dt_sp_buf.get();
|
||||
float * cs = cs_buf.get();
|
||||
float * CB = CB_buf.get();
|
||||
matmul_t * X_dt = X_dt_buf.get();
|
||||
matmul_t * B_weighted = B_w_buf.get();
|
||||
float * C_scaled = C_s_buf.get();
|
||||
float * s_cur = (float *)((char *)dst_d + s_off); // write state directly to dst
|
||||
|
||||
// Step 1: softplus(dt) and parallel prefix sum over full sequence
|
||||
{
|
||||
dim3 grid(n_head, n_seq);
|
||||
ssm_ssd_prepare_dt_kernel<SSM_SSD_DT_BLOCK, SSM_SSD_DT_MAX_ITEMS><<<grid, SSM_SSD_DT_BLOCK, 0, stream>>>(
|
||||
src2_d, dt_sp, cs, n_head, n_tok, dt_stride_tok, dt_stride_seq);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
||||
// Step 2: initialize running state from src0[ids[s]]
|
||||
{
|
||||
constexpr int BLOCK = 256;
|
||||
const int64_t state_size = d_state * head_dim * n_head;
|
||||
dim3 grid((state_size + BLOCK - 1) / BLOCK, n_seq);
|
||||
ssm_ssd_init_state_kernel<BLOCK><<<grid, BLOCK, 0, stream>>>(
|
||||
src0_d, src6_d, s_cur, state_size, s0_stride_seq);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
||||
// Step 3: chunked SSD loop
|
||||
// Per chunk: pre_matmul (incl. M) + 4 cuBLAS (CB, Y, S@C, state update) + scale_state
|
||||
cublasHandle_t handle = ctx.cublas_handle();
|
||||
CUBLAS_CHECK(cublasSetStream(handle, stream));
|
||||
const float alpha_one = 1.0f;
|
||||
const float beta_zero = 0.0f;
|
||||
const float beta_one = 1.0f;
|
||||
const int lda_C_src = C_stride_tok; // leading dim for C in CB = C^T @ B
|
||||
const int ldb_B_src = B_stride_tok; // leading dim for B in CB = C^T @ B
|
||||
|
||||
// Scratch buffer for causal M matrix, reused across chunks (max size at chunk_size)
|
||||
const int64_t n_M_max = chunk_size * chunk_size;
|
||||
ggml_cuda_pool_alloc<half> M_buf(ctx.pool(), n_M_max * n_head * n_seq);
|
||||
half * M_mat = M_buf.get();
|
||||
|
||||
for (int64_t k = 0; k < n_chunks; k++) {
|
||||
const int64_t chunk_offset = k * chunk_size;
|
||||
const int64_t chunk_len = (chunk_offset + chunk_size <= n_tok) ? chunk_size : (n_tok - chunk_offset);
|
||||
|
||||
// 3a: CB = C^T @ B per group
|
||||
for (int64_t s = 0; s < n_seq; s++) {
|
||||
const float * C_s = src5_d + s * C_stride_seq + chunk_offset * C_stride_tok;
|
||||
const float * B_s = src4_d + s * B_stride_seq + chunk_offset * B_stride_tok;
|
||||
float * CB_s = CB + s * chunk_len * chunk_len * n_group;
|
||||
|
||||
if (n_group == 1) {
|
||||
CUBLAS_CHECK(cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N,
|
||||
chunk_len, chunk_len, d_state,
|
||||
&alpha_one, C_s, lda_C_src, B_s, ldb_B_src,
|
||||
&beta_zero, CB_s, (int)chunk_len));
|
||||
} else {
|
||||
CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_T, CUBLAS_OP_N,
|
||||
chunk_len, chunk_len, d_state,
|
||||
&alpha_one,
|
||||
C_s, CUDA_R_32F, lda_C_src, d_state,
|
||||
B_s, CUDA_R_32F, ldb_B_src, d_state,
|
||||
&beta_zero,
|
||||
CB_s, CUDA_R_32F, (int)chunk_len, (long long)(chunk_len * chunk_len),
|
||||
n_group,
|
||||
CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
|
||||
}
|
||||
}
|
||||
|
||||
// 3b: prepare X_dt, B_weighted, C_scaled + materialize causal M matrix
|
||||
const int64_t n_M = chunk_len * chunk_len;
|
||||
{
|
||||
constexpr int BLOCK = 256;
|
||||
const int64_t n_xdt = chunk_len * head_dim;
|
||||
const int64_t n_bw = d_state * chunk_len;
|
||||
int64_t max_work = n_xdt;
|
||||
if (n_bw > max_work) max_work = n_bw;
|
||||
if (n_M > max_work) max_work = n_M;
|
||||
dim3 grid((max_work + BLOCK - 1) / BLOCK, n_head, n_seq);
|
||||
ssm_ssd_pre_matmul_kernel<BLOCK, matmul_t><<<grid, BLOCK, 0, stream>>>(
|
||||
cs, dt_sp, src3_d, src1_d, src4_d, src5_d,
|
||||
X_dt, B_weighted, C_scaled,
|
||||
CB, M_mat,
|
||||
chunk_len, head_dim, n_head, n_group, d_state, A_stride,
|
||||
x_stride_tok, x_stride_seq, B_stride_tok, B_stride_seq, C_stride_tok, C_stride_seq,
|
||||
chunk_offset, n_tok);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
||||
// 3c: dst = S_cur^T @ C_scaled (state contribution)
|
||||
{
|
||||
const int64_t stride_S = state_per_head;
|
||||
const int64_t stride_Cs = d_state * chunk_len;
|
||||
|
||||
for (int64_t s = 0; s < n_seq; s++) {
|
||||
float * dst_chunk = dst_d + s * d_inner * n_tok + chunk_offset * d_inner;
|
||||
|
||||
CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_T, CUBLAS_OP_N,
|
||||
head_dim, chunk_len, d_state,
|
||||
&alpha_one,
|
||||
s_cur + s * stride_S * n_head, CUDA_R_32F, d_state, stride_S,
|
||||
C_scaled + s * stride_Cs * n_head, CUDA_R_32F, d_state, stride_Cs,
|
||||
&beta_zero,
|
||||
dst_chunk, CUDA_R_32F, d_inner, head_dim,
|
||||
n_head,
|
||||
CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
|
||||
}
|
||||
}
|
||||
|
||||
// 3d: dst += X_dt @ M^T (intra-chunk contribution, adds to 3c result)
|
||||
// M is stored as M[t_out, t_in] (lower-triangular), transpose needed for Y = X @ M^T.
|
||||
{
|
||||
const int64_t stride_M = n_M;
|
||||
const int64_t stride_X_h = (int64_t)chunk_len * head_dim;
|
||||
|
||||
for (int64_t s = 0; s < n_seq; s++) {
|
||||
float * dst_chunk = dst_d + s * d_inner * n_tok + chunk_offset * d_inner;
|
||||
CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_N, CUBLAS_OP_T,
|
||||
head_dim, chunk_len, chunk_len,
|
||||
&alpha_one,
|
||||
X_dt + s * stride_X_h * n_head, matmul_dtype, head_dim, stride_X_h,
|
||||
M_mat + s * stride_M * n_head, matmul_dtype, chunk_len, stride_M,
|
||||
&beta_one,
|
||||
dst_chunk, CUDA_R_32F, d_inner, head_dim,
|
||||
n_head,
|
||||
CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
|
||||
}
|
||||
}
|
||||
|
||||
// 3e: s_cur = B_weighted @ X_dt^T + decay_total * s_cur_old (state update)
|
||||
{
|
||||
// Scale s_cur in-place by per-head decay_total BEFORE cuBLAS overwrites it
|
||||
constexpr int BLOCK = 256;
|
||||
dim3 grid((state_per_head + BLOCK - 1) / BLOCK, n_head, n_seq);
|
||||
ssm_ssd_scale_state_kernel<BLOCK><<<grid, BLOCK, 0, stream>>>(
|
||||
s_cur, cs, src3_d,
|
||||
d_state, head_dim, n_head,
|
||||
chunk_offset, chunk_len, n_tok, A_stride);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
// cuBLAS with beta=1: s_cur = B_weighted @ X_dt^T + 1.0 * s_cur (already scaled)
|
||||
const int64_t stride_Bw = d_state * chunk_len;
|
||||
const int64_t stride_X = chunk_len * head_dim;
|
||||
const int64_t stride_S = state_per_head;
|
||||
|
||||
for (int64_t s = 0; s < n_seq; s++) {
|
||||
// X_dt is d-fastest {hd, C}, read as OP_T to get {C, hd}
|
||||
CUBLAS_CHECK(cublasGemmStridedBatchedEx(handle, CUBLAS_OP_N, CUBLAS_OP_T,
|
||||
d_state, head_dim, chunk_len,
|
||||
&alpha_one,
|
||||
B_weighted + s * stride_Bw * n_head, matmul_dtype, d_state, stride_Bw,
|
||||
X_dt + s * stride_X * n_head, matmul_dtype, head_dim, stride_X,
|
||||
&beta_one,
|
||||
s_cur + s * stride_S * n_head, CUDA_R_32F, d_state, stride_S,
|
||||
n_head,
|
||||
CUBLAS_COMPUTE_32F, CUBLAS_GEMM_DEFAULT));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
|
||||
|
||||
void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const struct ggml_tensor * src0 = dst->src[0]; // s
|
||||
const struct ggml_tensor * src1 = dst->src[1]; // x
|
||||
|
|
@ -357,6 +795,49 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|||
GGML_ASSERT(src6->type == GGML_TYPE_I32);
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
||||
|
||||
// Byte strides are narrowed to int for both scan and SSD paths.
|
||||
GGML_ASSERT(src0->nb[2] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src0->nb[3] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src1->nb[2] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src1->nb[3] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src2->nb[1] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src2->nb[2] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src3->nb[1] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src4->nb[2] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src4->nb[3] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src5->nb[2] <= (size_t)INT_MAX);
|
||||
GGML_ASSERT(src5->nb[3] <= (size_t)INT_MAX);
|
||||
|
||||
#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
|
||||
// Mamba-2 with scalar A per head: use SSD matmul path for long sequences.
|
||||
// Requires NVIDIA Turing+ otherwise fallback to scan.
|
||||
const bool is_mamba2 = (src3->nb[1] == sizeof(float));
|
||||
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
|
||||
const bool use_ssd = is_mamba2 && n_t > SSM_SSD_MIN_TOKENS
|
||||
&& n_t <= SSM_SSD_MAX_TOKENS
|
||||
&& GGML_CUDA_CC_IS_NVIDIA(cc)
|
||||
&& cc >= GGML_CUDA_CC_TURING
|
||||
&& nr % 8 == 0; // cuBLAS requires 8-element (16-byte) alignment
|
||||
|
||||
if (use_ssd) {
|
||||
// ssm_ssd_init_state_kernel uses flat linear indexing within each sequence,
|
||||
// so src0 must be fully contiguous across all inner dimensions.
|
||||
// The scan path handles non-contiguous nb[2] via src0_nb2 but does not handle nb[1].
|
||||
GGML_ASSERT(src0->nb[1] == nc * sizeof(float));
|
||||
GGML_ASSERT(src0->nb[2] == nc * nr * sizeof(float));
|
||||
|
||||
ssm_scan_ssd_f32_cuda(ctx,
|
||||
src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d,
|
||||
(int64_t)(src0->nb[3] / sizeof(float)),
|
||||
(int)(src1->nb[2] / sizeof(float)), (int)(src1->nb[3] / sizeof(float)),
|
||||
(int)(src2->nb[1] / sizeof(float)), (int)(src2->nb[2] / sizeof(float)),
|
||||
(int)(src3->nb[1] / sizeof(float)),
|
||||
(int)(src4->nb[2] / sizeof(float)), (int)(src4->nb[3] / sizeof(float)),
|
||||
(int)(src5->nb[2] / sizeof(float)), (int)(src5->nb[3] / sizeof(float)),
|
||||
s_off, nc, nr, nh, ng, n_t, n_s);
|
||||
return;
|
||||
}
|
||||
#endif
|
||||
ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d,
|
||||
src0->nb[2], src0->nb[3], src1->nb[2], src1->nb[3], src2->nb[1], src2->nb[2],
|
||||
src3->nb[1], src4->nb[2], src4->nb[3], src5->nb[2], src5->nb[3],
|
||||
|
|
|
|||
|
|
@ -1252,6 +1252,21 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge(gg
|
|||
return res;
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_fwht(ggml_metal_library_t lib, int n) {
|
||||
char base[256];
|
||||
char name[256];
|
||||
|
||||
snprintf(base, 256, "kernel_fwht_f32_%d", n);
|
||||
snprintf(name, 256, "%s", base);
|
||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
// note: reuse the argsort kernel for top_k
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k(ggml_metal_library_t lib, const ggml_tensor * op) {
|
||||
assert(op->op == GGML_OP_TOP_K);
|
||||
|
|
|
|||
|
|
@ -139,6 +139,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id
|
|||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argmax (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_argsort_merge (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_fwht (ggml_metal_library_t lib, int n);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_top_k_merge (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_bin (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse );
|
||||
|
|
|
|||
|
|
@ -1157,6 +1157,10 @@ typedef struct {
|
|||
int32_t len;
|
||||
} ggml_metal_kargs_argsort_merge;
|
||||
|
||||
typedef struct {
|
||||
int32_t nrows;
|
||||
} ggml_metal_kargs_fwht;
|
||||
|
||||
typedef struct {
|
||||
int64_t ne0;
|
||||
float start;
|
||||
|
|
|
|||
|
|
@ -1979,6 +1979,46 @@ int ggml_metal_op_pool_1d(ggml_metal_op_t ctx, int idx) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
// supported FWHT sizes, must stay in sync with the
|
||||
// kernel_fwht_f32_<N> templates in ggml-metal.metal
|
||||
static bool ggml_metal_fwht_supported_size(int64_t n) {
|
||||
return n == 64 || n == 128 || n == 256 || n == 512;
|
||||
}
|
||||
|
||||
int ggml_metal_op_fwht(ggml_metal_op_t ctx, int idx) {
|
||||
ggml_tensor * op = ctx->node(idx);
|
||||
|
||||
ggml_metal_library_t lib = ctx->lib;
|
||||
ggml_metal_encoder_t enc = ctx->enc;
|
||||
|
||||
ggml_tensor * src1 = op->src[1];
|
||||
|
||||
const int64_t n = src1->ne[0];
|
||||
const int64_t nrows = ggml_nrows(src1);
|
||||
|
||||
ggml_metal_kargs_fwht args = {
|
||||
/*.nrows = */ (int32_t) nrows,
|
||||
};
|
||||
|
||||
auto pipeline = ggml_metal_library_get_pipeline_fwht(lib, n);
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(src1), 1);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 2);
|
||||
|
||||
const int th_max = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline);
|
||||
const int simd_size = 32;
|
||||
|
||||
int sg_per_tg = 2;
|
||||
sg_per_tg = std::min(sg_per_tg, th_max/simd_size);
|
||||
sg_per_tg = std::max(sg_per_tg, 1);
|
||||
|
||||
const int64_t n_tg = (nrows + sg_per_tg - 1) / sg_per_tg;
|
||||
ggml_metal_encoder_dispatch_threadgroups(enc, n_tg, 1, 1, 32*sg_per_tg, 1, 1);
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
||||
int ggml_metal_op_pool_2d(ggml_metal_op_t ctx, int idx) {
|
||||
ggml_tensor * op = ctx->node(idx);
|
||||
|
|
@ -2046,6 +2086,18 @@ int ggml_metal_op_mul_mat(ggml_metal_op_t ctx, int idx) {
|
|||
ggml_metal_library_t lib = ctx->lib;
|
||||
ggml_metal_encoder_t enc = ctx->enc;
|
||||
|
||||
const int32_t hint = ggml_get_op_params_i32(op, 1);
|
||||
|
||||
if (hint == GGML_HINT_SRC0_IS_HADAMARD) {
|
||||
if (op->src[1]->type == GGML_TYPE_F32 &&
|
||||
op->type == GGML_TYPE_F32 &&
|
||||
ggml_is_contiguous(op->src[1]) &&
|
||||
ggml_is_contiguous(op) &&
|
||||
ggml_are_same_shape(op->src[1], op) &&
|
||||
ggml_metal_fwht_supported_size(op->src[1]->ne[0])) {
|
||||
return ggml_metal_op_fwht(ctx, idx);
|
||||
}
|
||||
}
|
||||
const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx->dev);
|
||||
|
||||
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
|
||||
|
|
|
|||
|
|
@ -64,6 +64,7 @@ int ggml_metal_op_set (ggml_metal_op_t ctx, int idx);
|
|||
int ggml_metal_op_cpy (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_pool_1d (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_pool_2d (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_fwht (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_mul_mat (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_mul_mat_id (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_add_id (ggml_metal_op_t ctx, int idx);
|
||||
|
|
|
|||
|
|
@ -5762,7 +5762,7 @@ kernel void kernel_upscale_bicubic_f32(
|
|||
const float w_y2 = bicubic_weight1(1.0f - fd1);
|
||||
const float w_y3 = bicubic_weight2(2.0f - fd1);
|
||||
|
||||
const device const char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02;
|
||||
const device char * src_slice = src0 + i03 * args.nb03 + i02 * args.nb02;
|
||||
|
||||
device float * dst_ptr = (device float *)(dst + i3 * args.nb3 + i2 * args.nb2 + i1 * args.nb1);
|
||||
|
||||
|
|
@ -6172,6 +6172,68 @@ kernel void kernel_argsort_merge_f32_i32(
|
|||
template [[host_name("kernel_argsort_merge_f32_i32_asc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_ASC>;
|
||||
template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_DESC>;
|
||||
|
||||
template<int N>
|
||||
kernel void kernel_fwht_f32(
|
||||
constant ggml_metal_kargs_fwht & args,
|
||||
device const float * src,
|
||||
device float * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
|
||||
constexpr int NW = N_SIMDWIDTH;
|
||||
constexpr int NE = N / NW;
|
||||
|
||||
const float scale = 1.0f / sqrt((float) N);
|
||||
|
||||
const int sg_per_tg = ntg.x / NW;
|
||||
const int64_t r = tgpig.x * sg_per_tg + sgitg;
|
||||
if (r >= args.nrows) {
|
||||
return;
|
||||
}
|
||||
|
||||
src += r * N;
|
||||
dst += r * N;
|
||||
|
||||
const int lane = tiisg;
|
||||
|
||||
float reg[NE];
|
||||
for (int i = 0; i < NE; i++) {
|
||||
reg[i] = src[i*NW + lane]*scale;
|
||||
}
|
||||
for (int i = 1; i < NW; i *= 2) {
|
||||
for (int j = 0; j < NE; j++) {
|
||||
const float val = reg[j];
|
||||
const float val2 = simd_shuffle_xor(val, i);
|
||||
reg[j] = (lane & i) == 0 ? val2 + val : val2 - val;
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = NW; i < N; i *= 2) {
|
||||
const int step = i / NW;
|
||||
for (int j = 0; j < NE; j += (2 * step)) {
|
||||
for (int k = 0; k < step; k++) {
|
||||
const float x = reg[j + k ];
|
||||
const float y = reg[j + k + step];
|
||||
reg[j + k] = x + y;
|
||||
reg[j + k + step] = x - y;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for (int i = 0; i < NE; i++) {
|
||||
dst[i*NW + lane] = reg[i];
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_fwht_f32<64>) kernel_fwht_t;
|
||||
|
||||
template [[host_name("kernel_fwht_f32_64")]] kernel kernel_fwht_t kernel_fwht_f32<64>;
|
||||
template [[host_name("kernel_fwht_f32_128")]] kernel kernel_fwht_t kernel_fwht_f32<128>;
|
||||
template [[host_name("kernel_fwht_f32_256")]] kernel kernel_fwht_t kernel_fwht_f32<256>;
|
||||
template [[host_name("kernel_fwht_f32_512")]] kernel kernel_fwht_t kernel_fwht_f32<512>;
|
||||
|
||||
constant bool FC_flash_attn_ext_pad_has_mask [[function_constant(FC_FLASH_ATTN_EXT_PAD + 0)]];
|
||||
|
||||
constant int32_t FC_flash_attn_ext_pad_ncpsg [[function_constant(FC_FLASH_ATTN_EXT_PAD + 25)]];
|
||||
|
|
|
|||
|
|
@ -74,6 +74,7 @@ enum rpc_cmd {
|
|||
RPC_CMD_HELLO,
|
||||
RPC_CMD_DEVICE_COUNT,
|
||||
RPC_CMD_GRAPH_RECOMPUTE,
|
||||
RPC_CMD_MEMSET_TENSOR,
|
||||
RPC_CMD_COUNT,
|
||||
};
|
||||
|
||||
|
|
@ -155,6 +156,13 @@ struct rpc_msg_buffer_clear_req {
|
|||
uint8_t value;
|
||||
};
|
||||
|
||||
struct rpc_msg_memset_tensor_req {
|
||||
rpc_tensor tensor;
|
||||
uint64_t offset;
|
||||
uint64_t size;
|
||||
uint8_t value;
|
||||
};
|
||||
|
||||
struct rpc_msg_set_tensor_hash_req {
|
||||
rpc_tensor tensor;
|
||||
uint64_t offset;
|
||||
|
|
@ -465,6 +473,19 @@ static enum ggml_status ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_
|
|||
return GGML_STATUS_SUCCESS;
|
||||
}
|
||||
|
||||
static void ggml_backend_rpc_buffer_memset_tensor(
|
||||
ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
|
||||
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
|
||||
rpc_msg_memset_tensor_req request = {
|
||||
/* .tensor = */ serialize_tensor(tensor),
|
||||
/* .offset = */ offset,
|
||||
/* .size = */ size,
|
||||
/* .value = */ value,
|
||||
};
|
||||
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_MEMSET_TENSOR, &request, sizeof(request), nullptr, 0);
|
||||
RPC_STATUS_ASSERT(status);
|
||||
}
|
||||
|
||||
static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
|
||||
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
|
||||
rpc_tensor rpc_tensor = serialize_tensor(tensor);
|
||||
|
|
@ -534,7 +555,7 @@ static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = {
|
|||
/* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer,
|
||||
/* .get_base = */ ggml_backend_rpc_buffer_get_base,
|
||||
/* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor,
|
||||
/* .memset_tensor = */ NULL,
|
||||
/* .memset_tensor = */ ggml_backend_rpc_buffer_memset_tensor,
|
||||
/* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor,
|
||||
/* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor,
|
||||
/* .set_tensor_2d = */ NULL,
|
||||
|
|
@ -834,6 +855,7 @@ public:
|
|||
bool buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response);
|
||||
bool free_buffer(const rpc_msg_free_buffer_req & request);
|
||||
bool buffer_clear(const rpc_msg_buffer_clear_req & request);
|
||||
bool memset_tensor(const rpc_msg_memset_tensor_req & request);
|
||||
bool set_tensor(const std::vector<uint8_t> & input);
|
||||
bool set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response);
|
||||
bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
|
||||
|
|
@ -992,6 +1014,52 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) {
|
|||
return true;
|
||||
}
|
||||
|
||||
bool rpc_server::memset_tensor(const rpc_msg_memset_tensor_req & request) {
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_context_ptr ctx_ptr { ggml_init(params) };
|
||||
GGML_ASSERT(ctx_ptr != nullptr);
|
||||
ggml_context * ctx = ctx_ptr.get();
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
|
||||
if (tensor == nullptr || tensor->buffer == nullptr) {
|
||||
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
const uint64_t tensor_size = ggml_nbytes(tensor);
|
||||
if (request.offset > tensor_size || request.size > tensor_size - request.offset) {
|
||||
GGML_LOG_ERROR("[%s] tensor region (offset=%" PRIu64 ", size=%" PRIu64 ") out of tensor bounds [0, %" PRIu64 ")\n",
|
||||
__func__, request.offset, request.size, tensor_size);
|
||||
return false;
|
||||
}
|
||||
|
||||
const uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(tensor->buffer);
|
||||
const uint64_t buffer_size = ggml_backend_buffer_get_size(tensor->buffer);
|
||||
if (request.tensor.data < buffer_start) {
|
||||
GGML_LOG_ERROR("[%s] tensor data before buffer start\n", __func__);
|
||||
return false;
|
||||
}
|
||||
const uint64_t data_offset = request.tensor.data - buffer_start;
|
||||
if (data_offset > buffer_size ||
|
||||
request.offset > buffer_size - data_offset ||
|
||||
request.size > buffer_size - data_offset - request.offset) {
|
||||
GGML_LOG_ERROR("[%s] tensor region out of buffer bounds\n", __func__);
|
||||
return false;
|
||||
}
|
||||
if (tensor->buffer->iface.memset_tensor == nullptr) {
|
||||
GGML_LOG_ERROR("[%s] memset not implemented by backend buffer\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 ", value: %u\n",
|
||||
__func__, (void *) tensor->buffer, tensor->data, request.offset, request.size, request.value);
|
||||
ggml_backend_tensor_memset(tensor, request.value, request.offset, request.size);
|
||||
return true;
|
||||
}
|
||||
|
||||
ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) {
|
||||
// Validate tensor type before using it
|
||||
if (tensor->type >= GGML_TYPE_COUNT) {
|
||||
|
|
@ -1588,6 +1656,19 @@ static void rpc_serve_client(const std::vector<ggml_backend_t> & backends, const
|
|||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_MEMSET_TENSOR: {
|
||||
rpc_msg_memset_tensor_req request;
|
||||
if (!recv_msg(sock, &request, sizeof(request))) {
|
||||
return;
|
||||
}
|
||||
if (!server.memset_tensor(request)) {
|
||||
return;
|
||||
}
|
||||
if (!send_msg(sock, nullptr, 0)) {
|
||||
return;
|
||||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_SET_TENSOR: {
|
||||
std::vector<uint8_t> input;
|
||||
if (!recv_msg(sock, input)) {
|
||||
|
|
|
|||
|
|
@ -3496,7 +3496,7 @@ struct vk_fa_tuning_params {
|
|||
};
|
||||
|
||||
static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type);
|
||||
static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16);
|
||||
static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16, ggml_type v_type = GGML_TYPE_F16);
|
||||
|
||||
static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) {
|
||||
|
||||
|
|
@ -3652,7 +3652,7 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_
|
|||
bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) ||
|
||||
(!f32acc && device->coopmat_support_16x16x16_f16acc);
|
||||
const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
|
||||
bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type);
|
||||
bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type, v_type);
|
||||
|
||||
if (!shape_ok || !shmem_ok) {
|
||||
path = FA_SCALAR;
|
||||
|
|
@ -3664,11 +3664,6 @@ static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_
|
|||
path = FA_SCALAR;
|
||||
}
|
||||
|
||||
// Q1_0 K/V is only implemented on coopmat2 (flash_attn_cm2); there is no scalar FA shader for it.
|
||||
if ((k_type == GGML_TYPE_Q1_0 || v_type == GGML_TYPE_Q1_0) && device->coopmat2) {
|
||||
path = FA_COOPMAT2;
|
||||
}
|
||||
|
||||
switch (path) {
|
||||
case FA_SCALAR:
|
||||
return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc);
|
||||
|
|
@ -3910,16 +3905,27 @@ static uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_dev
|
|||
return 0; // If no matching configuration is found
|
||||
}
|
||||
|
||||
// Whether scalar flash attention will use the MMQ path for the given k_type.
|
||||
static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type) {
|
||||
// Whether scalar flash attention will use the MMQ path for the given K/V types.
|
||||
static bool ggml_vk_fa_type_needs_shmem(ggml_type type) {
|
||||
switch (type) {
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
return true;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type, ggml_type v_type) {
|
||||
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
|
||||
return device->integer_dot_product && device->subgroup_clustered &&
|
||||
!ggml_vk_fa_type_needs_shmem(v_type) &&
|
||||
(k_type == GGML_TYPE_Q4_0 || k_type == GGML_TYPE_Q4_1 ||
|
||||
k_type == GGML_TYPE_Q5_0 || k_type == GGML_TYPE_Q5_1 ||
|
||||
k_type == GGML_TYPE_Q8_0);
|
||||
#else
|
||||
GGML_UNUSED(device);
|
||||
GGML_UNUSED(k_type);
|
||||
GGML_UNUSED(v_type);
|
||||
return false;
|
||||
#endif
|
||||
}
|
||||
|
|
@ -4252,7 +4258,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
|
|||
const bool fa_ds = fa.first.subgroup_size == 0;
|
||||
|
||||
const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16;
|
||||
const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type);
|
||||
const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type, fa.first.v_type);
|
||||
const void * spv_data = nullptr;
|
||||
size_t spv_size = 0;
|
||||
const char *name = nullptr;
|
||||
|
|
@ -10413,7 +10419,6 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
|
|||
|
||||
static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) {
|
||||
GGML_UNUSED(f32acc);
|
||||
GGML_UNUSED(v_type);
|
||||
// Needs to be kept up to date on shader changes
|
||||
const uint32_t wg_size = params.workgroup_size;
|
||||
const uint32_t Br = params.block_rows;
|
||||
|
|
@ -10422,13 +10427,15 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
|
|||
// BF16 uses the fp32 shader (FLOAT_TYPE=float)
|
||||
const uint32_t float_type_size = (device->fp16 && k_type != GGML_TYPE_BF16) ? sizeof(ggml_fp16_t) : sizeof(float);
|
||||
|
||||
const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type);
|
||||
const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type, v_type);
|
||||
|
||||
// tmpsh is overestimated slightly
|
||||
const uint32_t tmpsh = wg_size * sizeof(float);
|
||||
const uint32_t tmpshv4 = wg_size * 4 * float_type_size;
|
||||
|
||||
const uint32_t masksh = Bc * (Br + 1) * float_type_size;
|
||||
// DATA_A_IQ4_NL is compiled into the FA shaders unconditionally, so its shared table is always allocated.
|
||||
const uint32_t iq_shmem = 16 * float_type_size;
|
||||
|
||||
uint32_t Qf, kvsh, kblocksh_size;
|
||||
if (mmq) {
|
||||
|
|
@ -10453,7 +10460,7 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
|
|||
kblocksh_size = 0;
|
||||
}
|
||||
|
||||
const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf + kvsh + kblocksh_size;
|
||||
const uint32_t total_size = tmpsh + tmpshv4 + masksh + iq_shmem + Qf + kvsh + kblocksh_size;
|
||||
const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
|
||||
|
||||
VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", mmq=" << mmq << ", total_size=" << total_size << ", supported=" << supported);
|
||||
|
|
@ -10461,7 +10468,8 @@ static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, con
|
|||
return supported;
|
||||
}
|
||||
|
||||
static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type) {
|
||||
static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) {
|
||||
GGML_UNUSED(v_type);
|
||||
// Needs to be kept up to date on shader changes
|
||||
const uint32_t Br = params.block_rows;
|
||||
const uint32_t Bc = params.block_cols;
|
||||
|
|
@ -10477,6 +10485,8 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
|
|||
const uint32_t f16vec4 = 8;
|
||||
|
||||
const uint32_t tmpsh = (Bc / MatBc) * sizeof(float);
|
||||
// DATA_A_IQ4_NL is compiled into the FA shaders unconditionally, so its shared table is always allocated.
|
||||
const uint32_t iq_shmem = 16 * sizeof(ggml_fp16_t);
|
||||
|
||||
const uint32_t qstride = hsk_pad / 4 + 2;
|
||||
const uint32_t Qf = Br * qstride * f16vec4;
|
||||
|
|
@ -10498,7 +10508,7 @@ static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, co
|
|||
|
||||
const uint32_t slope = Br * acctype;
|
||||
|
||||
const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + pvsh + slope;
|
||||
const uint32_t total_size = tmpsh + iq_shmem + Qf + Psh + sfsh + ksh + pvsh + slope;
|
||||
const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize;
|
||||
|
||||
VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported);
|
||||
|
|
@ -17650,7 +17660,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
|
|||
if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
|
||||
return false;
|
||||
}
|
||||
auto fa_kv_ok = [coopmat2](ggml_type t) {
|
||||
auto fa_kv_ok = [](ggml_type t) {
|
||||
switch (t) {
|
||||
case GGML_TYPE_F32:
|
||||
case GGML_TYPE_F16:
|
||||
|
|
@ -17660,9 +17670,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
|
|||
case GGML_TYPE_Q5_0:
|
||||
case GGML_TYPE_Q4_1:
|
||||
case GGML_TYPE_Q4_0:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
return true;
|
||||
case GGML_TYPE_Q1_0:
|
||||
return coopmat2;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -80,7 +80,9 @@ shared vec4 occupancy_limiter[LIMIT_OCCUPANCY_SHMEM > 0 ? LIMIT_OCCUPANCY_SHMEM
|
|||
|
||||
void main() {
|
||||
#ifdef NEEDS_INIT_IQ_SHMEM
|
||||
init_iq_shmem(gl_WorkGroupSize);
|
||||
if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) {
|
||||
init_iq_shmem(gl_WorkGroupSize);
|
||||
}
|
||||
#endif
|
||||
|
||||
init_indices();
|
||||
|
|
|
|||
|
|
@ -97,8 +97,8 @@ layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];};
|
|||
#define FA_TYPE_Q5_0 6u
|
||||
#define FA_TYPE_Q5_1 7u
|
||||
#define FA_TYPE_Q8_0 8u
|
||||
#define FA_TYPE_IQ4_NL 20u
|
||||
#define FA_TYPE_BF16 30u
|
||||
#define FA_TYPE_Q1_0 41u
|
||||
|
||||
#if defined(BFLOAT16)
|
||||
#define O_TYPE float
|
||||
|
|
@ -120,8 +120,8 @@ uint fa_block_elems(uint ty) {
|
|||
case FA_TYPE_Q5_0: return uint(QUANT_K_Q5_0);
|
||||
case FA_TYPE_Q5_1: return uint(QUANT_K_Q5_1);
|
||||
case FA_TYPE_Q8_0: return uint(QUANT_K_Q8_0);
|
||||
case FA_TYPE_IQ4_NL: return uint(QUANT_K_IQ4_NL);
|
||||
case FA_TYPE_BF16: return 1u;
|
||||
case FA_TYPE_Q1_0: return uint(QUANT_K_Q1_0); // cm2-only, harmless elsewhere
|
||||
default: return 1u;
|
||||
}
|
||||
}
|
||||
|
|
@ -140,6 +140,13 @@ uint fa_quant_r_mmq(uint ty) {
|
|||
}
|
||||
}
|
||||
|
||||
bool fa_type_needs_shmem(uint ty) {
|
||||
switch (ty) {
|
||||
case FA_TYPE_IQ4_NL: return true;
|
||||
default: return false;
|
||||
}
|
||||
}
|
||||
|
||||
// These can't be `const` globals because GLSL forbids function calls in global
|
||||
// const initializers, even when the spec constants would let the driver fold
|
||||
// them. Macros expand at the use site and fold after specialization.
|
||||
|
|
|
|||
|
|
@ -64,7 +64,9 @@ shared ACC_TYPE slope[Br];
|
|||
|
||||
void main() {
|
||||
#ifdef NEEDS_INIT_IQ_SHMEM
|
||||
init_iq_shmem(gl_WorkGroupSize);
|
||||
if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) {
|
||||
init_iq_shmem(gl_WorkGroupSize);
|
||||
}
|
||||
#endif
|
||||
|
||||
init_indices();
|
||||
|
|
|
|||
|
|
@ -46,7 +46,7 @@ float16_t faDecodeK(const decodeBufFA_K bl_in, const uint blockCoords[2], const
|
|||
case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock);
|
||||
default: return float16_t(0);
|
||||
}
|
||||
}
|
||||
|
|
@ -59,7 +59,7 @@ float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const
|
|||
case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock);
|
||||
default: return float16_t(0);
|
||||
}
|
||||
}
|
||||
|
|
@ -67,26 +67,26 @@ float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const
|
|||
// V=4 vector decode for K/V; dispatches to per-format _v decoders.
|
||||
f16vec4 faDecodeKVector(const decodeBufFA_K bl_in, const uint blockCoords[2], const uint coordInBlock[2]) {
|
||||
switch (FaTypeK) {
|
||||
case 0u: return f16vec4(decodeBufF32(bl_in).block);
|
||||
case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
|
||||
case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
|
||||
case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_F32: return f16vec4(decodeBufF32(bl_in).block);
|
||||
case FA_TYPE_Q4_0: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q4_1: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_0: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_1: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q8_0: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL_v(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock);
|
||||
default: return f16vec4(0);
|
||||
}
|
||||
}
|
||||
|
||||
f16vec4 faDecodeVVector(const decodeBufFA_V bl_in, const uint blockCoords[2], const uint coordInBlock[2]) {
|
||||
switch (FaTypeV) {
|
||||
case 0u: return f16vec4(decodeBufF32(bl_in).block);
|
||||
case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
|
||||
case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
|
||||
case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_F32: return f16vec4(decodeBufF32(bl_in).block);
|
||||
case FA_TYPE_Q4_0: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q4_1: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_0: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q5_1: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_Q8_0: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock);
|
||||
case FA_TYPE_IQ4_NL: return dequantFuncIQ4_NL_v(decodeBufIQ4_NL(bl_in), blockCoords, coordInBlock);
|
||||
default: return f16vec4(0);
|
||||
}
|
||||
}
|
||||
|
|
@ -169,6 +169,12 @@ ACC_TYPE perElemOpNonGqaSplitKStoreCol0(const in uint32_t r, const in uint32_t c
|
|||
}
|
||||
|
||||
void main() {
|
||||
#ifdef NEEDS_INIT_IQ_SHMEM
|
||||
if (fa_type_needs_shmem(FaTypeK) || fa_type_needs_shmem(FaTypeV)) {
|
||||
init_iq_shmem(gl_WorkGroupSize);
|
||||
}
|
||||
#endif
|
||||
|
||||
init_indices();
|
||||
|
||||
tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV);
|
||||
|
|
@ -302,7 +308,7 @@ void main() {
|
|||
coopmat<FLOAT_TYPE, gl_ScopeWorkgroup, HSK_pad, Bc, gl_MatrixUseB> K_T;
|
||||
|
||||
uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13;
|
||||
// F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Q4/Q8 family: bs_k==32. Q1_0: bs_k==128.
|
||||
// F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Quantized types: bs_k==32.
|
||||
#if defined(BFLOAT16)
|
||||
coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose);
|
||||
#else
|
||||
|
|
|
|||
|
|
@ -27,6 +27,8 @@ layout (binding = 1) readonly buffer K_PACKED_Q5_1 { block_q5_1_packed16 data[];
|
|||
layout (binding = 2) readonly buffer V_PACKED_Q5_1 { block_q5_1_packed16 data[]; } v_packed_q5_1;
|
||||
layout (binding = 1) readonly buffer K_PACKED_Q8_0 { block_q8_0_packed16 data[]; } k_packed_q8_0;
|
||||
layout (binding = 2) readonly buffer V_PACKED_Q8_0 { block_q8_0_packed16 data[]; } v_packed_q8_0;
|
||||
layout (binding = 1) readonly buffer K_PACKED_IQ4_NL { block_iq4_nl_packed16 data[]; } k_packed_iq4_nl;
|
||||
layout (binding = 2) readonly buffer V_PACKED_IQ4_NL { block_iq4_nl_packed16 data[]; } v_packed_iq4_nl;
|
||||
|
||||
layout (binding = 1) readonly buffer K_PACKED_BF16 { u16vec4 data[]; } k_packed_bf16;
|
||||
layout (binding = 2) readonly buffer V_PACKED_BF16 { u16vec4 data[]; } v_packed_bf16;
|
||||
|
|
@ -102,6 +104,17 @@ layout (binding = 1) readonly buffer K_PACKED_Q5_1_P32 { block_q5_1_packed32 dat
|
|||
return FLOAT_TYPE(BUF.data[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y); \
|
||||
}
|
||||
|
||||
#define FA_DEQUANT4_IQ4_NL(BUF) { \
|
||||
const uint shift = (iqs & 0x10) >> 2; \
|
||||
const uint qs_i = (iqs & 0xC) >> 1; \
|
||||
const uint qsw = uint(BUF.data[a_offset + ib].qs[qs_i]) \
|
||||
| (uint(BUF.data[a_offset + ib].qs[qs_i + 1u]) << 16); \
|
||||
const FLOAT_TYPE d = FLOAT_TYPE(BUF.data[a_offset + ib].d); \
|
||||
const u8vec4 q = unpack8((qsw >> shift) & 0x0F0F0F0Fu); \
|
||||
return d * FLOAT_TYPEV4(kvalues_iq4nl[q.x], kvalues_iq4nl[q.y], \
|
||||
kvalues_iq4nl[q.z], kvalues_iq4nl[q.w]); \
|
||||
}
|
||||
|
||||
#define FA_DEQUANT4_BF16(BUF) \
|
||||
return FLOAT_TYPEV4(bf16_to_fp32(uvec4(BUF.data[(a_offset + ib) / 4])));
|
||||
|
||||
|
|
@ -114,6 +127,7 @@ FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
|
|||
case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(k_packed_q5_0)
|
||||
case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(k_packed_q5_1)
|
||||
case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(k_packed_q8_0)
|
||||
case FA_TYPE_IQ4_NL: FA_DEQUANT4_IQ4_NL(k_packed_iq4_nl)
|
||||
case FA_TYPE_BF16: FA_DEQUANT4_BF16(k_packed_bf16)
|
||||
}
|
||||
} else {
|
||||
|
|
@ -124,6 +138,7 @@ FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) {
|
|||
case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(v_packed_q5_0)
|
||||
case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(v_packed_q5_1)
|
||||
case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(v_packed_q8_0)
|
||||
case FA_TYPE_IQ4_NL: FA_DEQUANT4_IQ4_NL(v_packed_iq4_nl)
|
||||
case FA_TYPE_BF16: FA_DEQUANT4_BF16(v_packed_bf16)
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -699,6 +699,8 @@ void process_shaders() {
|
|||
fa_base_dict["ACC_TYPE"] = fp16 && f16acc ? "float16_t" : "float";
|
||||
fa_base_dict["ACC_TYPEV2"] = fp16 && f16acc ? "f16vec2" : "vec2";
|
||||
fa_base_dict["ACC_TYPEV4"] = fp16 && f16acc ? "f16vec4" : "vec4";
|
||||
// Compile IQ4_NL support into all FA variants so its shared LUT is available when K or V uses it.
|
||||
fa_base_dict["DATA_A_IQ4_NL"] = "1";
|
||||
if (fp16 && f16acc) {
|
||||
fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)";
|
||||
}
|
||||
|
|
|
|||
|
|
@ -7870,7 +7870,9 @@ void ggml_set_input(struct ggml_tensor * tensor) {
|
|||
}
|
||||
|
||||
void ggml_set_output(struct ggml_tensor * tensor) {
|
||||
tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
|
||||
for (struct ggml_tensor * cur = tensor; cur != NULL; cur = cur->view_src) {
|
||||
cur->flags |= GGML_TENSOR_FLAG_OUTPUT;
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_set_param(struct ggml_tensor * tensor) {
|
||||
|
|
|
|||
|
|
@ -145,6 +145,8 @@ class Keys:
|
|||
TOKEN_SHIFT_COUNT = "{arch}.token_shift_count"
|
||||
INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step"
|
||||
FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval"
|
||||
NUM_LOOPS = "{arch}.num_loops"
|
||||
SKIP_LOOP_FINAL_NORM = "{arch}.skip_loop_final_norm"
|
||||
HASH_LAYER_COUNT = "{arch}.hash_layer_count"
|
||||
ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale"
|
||||
ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx"
|
||||
|
|
@ -159,6 +161,7 @@ class Keys:
|
|||
TARGET_HIDDEN_SIZE = "{arch}.target_hidden_size"
|
||||
BLOCK_SIZE = "{arch}.block_size"
|
||||
NORM_BEFORE_RESIDUAL = "{arch}.norm_before_residual"
|
||||
NORM_BEFORE_FC = "{arch}.norm_before_fc"
|
||||
|
||||
class Attention:
|
||||
HEAD_COUNT = "{arch}.attention.head_count"
|
||||
|
|
@ -370,10 +373,17 @@ class Keys:
|
|||
FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length"
|
||||
PROJECTION_DIM = "clip.audio.projection_dim"
|
||||
BLOCK_COUNT = "clip.audio.block_count"
|
||||
SUBSAMPLING_FACTOR = "clip.audio.subsampling_factor"
|
||||
CHUNK_SIZE = "clip.audio.chunk_size"
|
||||
CONV_KERNEL_SIZE = "clip.audio.conv_kernel_size"
|
||||
MAX_POS_EMB = "clip.audio.max_pos_emb"
|
||||
FEATURE_LAYERS = "clip.audio.feature_layer" # Granite Speech Plus
|
||||
RVQ_NUM_QUANTIZERS = "clip.audio.rvq.num_quantizers"
|
||||
RVQ_CODEBOOK_SIZE = "clip.audio.rvq.codebook_size"
|
||||
WA_PATTERN_MODE = "clip.audio.wa_pattern_mode" # per-layer -1 (full) / 0 (windowed)
|
||||
WINDOW_SIZE = "clip.audio.window_size"
|
||||
LOCAL_BLOCK_COUNT = "clip.audio.local_block_count" # mimo-v2.5: input_local_transformer layer count
|
||||
LOCAL_GROUP_SIZE = "clip.audio.local_group_size" # mimo-v2.5: input_local_transformer grouping size
|
||||
|
||||
class Attention:
|
||||
HEAD_COUNT = "clip.audio.attention.head_count"
|
||||
|
|
@ -545,6 +555,7 @@ class MODEL_ARCH(IntEnum):
|
|||
KIMI_LINEAR = auto()
|
||||
TALKIE = auto()
|
||||
MELLUM = auto()
|
||||
NANBEIGE = auto()
|
||||
|
||||
|
||||
class VISION_PROJECTOR_TYPE(IntEnum):
|
||||
|
|
@ -942,6 +953,9 @@ class MODEL_TENSOR(IntEnum):
|
|||
A_ENC_FFN_SCALE_1 = auto() # gemma3n
|
||||
A_ENC_FFN_GATE_1 = auto() # lfm2, gemma3n
|
||||
A_ENC_FFN_DOWN_1 = auto() # lfm2, gemma3n
|
||||
A_ENC_DOWNSAMPLE_CONV = auto() # mimo-audio-tokenizer: post-transformer downsample conv
|
||||
A_ENC_DOWNSAMPLE_NORM = auto() # mimo-audio-tokenizer: post-transformer downsample norm
|
||||
A_ENC_RVQ_CODEBOOK = auto() # mimo-audio-tokenizer: residual vector quantizer codebook, per quantizer index
|
||||
A_MMPROJ = auto()
|
||||
A_MMPROJ_FC = auto()
|
||||
A_MM_NORM_PRE = auto()
|
||||
|
|
@ -950,6 +964,17 @@ class MODEL_TENSOR(IntEnum):
|
|||
A_MM_HARD_EMB_NORM = auto() # gemma3n
|
||||
A_MM_SOFT_EMB_NORM = auto() # gemma3n
|
||||
A_MM_INP_PROJ = auto() # gemma3n
|
||||
A_MM_CODE_EMBD = auto() # mimo: text-side RVQ code embedding table ("text codebook"), merged 3D [n_channels, vocab, dim]
|
||||
A_MM_LOCAL_ATTN_Q = auto() # mimo: input_local_transformer (LLM-side connector)
|
||||
A_MM_LOCAL_ATTN_K = auto()
|
||||
A_MM_LOCAL_ATTN_V = auto()
|
||||
A_MM_LOCAL_ATTN_OUT = auto()
|
||||
A_MM_LOCAL_FFN_GATE = auto()
|
||||
A_MM_LOCAL_FFN_UP = auto()
|
||||
A_MM_LOCAL_FFN_DOWN = auto()
|
||||
A_MM_LOCAL_LN1 = auto()
|
||||
A_MM_LOCAL_LN2 = auto()
|
||||
A_MM_LOCAL_NORM = auto() # final norm after all input_local_transformer layers
|
||||
A_PER_DIM_K_SCALE = auto() # gemma4
|
||||
A_PER_DIM_SCALE = auto() # gemma4
|
||||
# nextn/mtp
|
||||
|
|
@ -964,6 +989,10 @@ class MODEL_TENSOR(IntEnum):
|
|||
# eagle3
|
||||
FC = auto() # feature fusion layer
|
||||
D2T = auto() # draft to target vocabulary mapping
|
||||
# dspark
|
||||
DSPARK_MARKOV_W1 = auto() # markov head: prev-token embed
|
||||
DSPARK_MARKOV_W2 = auto() # markov head: bias projection
|
||||
DSPARK_CONF_PROJ = auto() # confidence head
|
||||
# lfm2 audio
|
||||
A_ENC_NORM_CONV = auto()
|
||||
A_ENC_LINEAR_POS = auto()
|
||||
|
|
@ -974,6 +1003,10 @@ class MODEL_TENSOR(IntEnum):
|
|||
A_ENC_CONV_NORM = auto() # SSM conv
|
||||
A_ENC_CONV_PW1 = auto()
|
||||
A_ENC_CONV_PW2 = auto()
|
||||
A_ENC_CONV_NORM_MEAN = auto() # parakeet
|
||||
A_ENC_CONV_NORM_VAR = auto() # parakeet
|
||||
A_ENC_MEL_FILTERS = auto() # parakeet
|
||||
A_ENC_WINDOW = auto() # parakeet
|
||||
A_CTC_OUT = auto()
|
||||
A_CTC_OUT_MID = auto()
|
||||
A_ENC_ATTN_REL_POS_EMB = auto()
|
||||
|
|
@ -1134,6 +1167,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.KIMI_LINEAR: "kimi-linear",
|
||||
MODEL_ARCH.TALKIE: "talkie",
|
||||
MODEL_ARCH.MELLUM: "mellum",
|
||||
MODEL_ARCH.NANBEIGE: "nanbeige",
|
||||
}
|
||||
|
||||
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
|
||||
|
|
@ -1528,6 +1562,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
|||
MODEL_TENSOR.A_ENC_FFN_UP_1: "a.blk.{bid}.ffn_up_1",
|
||||
MODEL_TENSOR.A_ENC_FFN_GATE_1: "a.blk.{bid}.ffn_gate_1",
|
||||
MODEL_TENSOR.A_ENC_FFN_DOWN_1: "a.blk.{bid}.ffn_down_1",
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: "a.downsample.conv",
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: "a.downsample.norm",
|
||||
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: "a.rvq.codebook",
|
||||
MODEL_TENSOR.A_MMPROJ: "mm.a.mlp.{bid}",
|
||||
MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc",
|
||||
MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre",
|
||||
|
|
@ -1536,6 +1573,17 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
|||
MODEL_TENSOR.A_MM_SOFT_EMB_NORM: "mm.a.soft_emb_norm", # gemma3n
|
||||
MODEL_TENSOR.A_MM_EMBEDDING: "mm.a.embedding", # gemma3n
|
||||
MODEL_TENSOR.A_MM_HARD_EMB_NORM: "mm.a.hard_emb_norm", # gemma3n
|
||||
MODEL_TENSOR.A_MM_CODE_EMBD: "mm.a.code_embd",
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: "mm.a.local_blk.{bid}.attn_q",
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_K: "mm.a.local_blk.{bid}.attn_k",
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_V: "mm.a.local_blk.{bid}.attn_v",
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: "mm.a.local_blk.{bid}.attn_out",
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: "mm.a.local_blk.{bid}.ffn_gate",
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_UP: "mm.a.local_blk.{bid}.ffn_up",
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: "mm.a.local_blk.{bid}.ffn_down",
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN1: "mm.a.local_blk.{bid}.ln1",
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN2: "mm.a.local_blk.{bid}.ln2",
|
||||
MODEL_TENSOR.A_MM_LOCAL_NORM: "mm.a.local_norm",
|
||||
MODEL_TENSOR.A_PER_DIM_K_SCALE: "a.blk.{bid}.per_dim_k_scale", # gemma4
|
||||
MODEL_TENSOR.A_PER_DIM_SCALE: "a.blk.{bid}.per_dim_scale", # gemma4
|
||||
# lfm2 audio
|
||||
|
|
@ -1548,6 +1596,10 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
|||
MODEL_TENSOR.A_ENC_CONV_NORM: "a.blk.{bid}.conv_norm",
|
||||
MODEL_TENSOR.A_ENC_CONV_PW1: "a.blk.{bid}.conv_pw1",
|
||||
MODEL_TENSOR.A_ENC_CONV_PW2: "a.blk.{bid}.conv_pw2",
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: "a.blk.{bid}.conv_norm_mean",
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_VAR: "a.blk.{bid}.conv_norm_var",
|
||||
MODEL_TENSOR.A_ENC_MEL_FILTERS: "a.mel_filters",
|
||||
MODEL_TENSOR.A_ENC_WINDOW: "a.window",
|
||||
MODEL_TENSOR.A_CTC_OUT: "a.enc_ctc_out",
|
||||
MODEL_TENSOR.A_CTC_OUT_MID: "a.enc_ctc_out_mid",
|
||||
MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: "a.blk.{bid}.attn_rel_pos_emb",
|
||||
|
|
@ -1578,6 +1630,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
|||
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head",
|
||||
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm",
|
||||
MODEL_TENSOR.FC: "fc",
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W1: "markov_w1",
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W2: "markov_w2",
|
||||
MODEL_TENSOR.DSPARK_CONF_PROJ: "conf_proj",
|
||||
MODEL_TENSOR.D2T: "d2t",
|
||||
}
|
||||
|
||||
|
|
@ -1737,10 +1792,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.A_ENC_FFN_UP_1,
|
||||
MODEL_TENSOR.A_ENC_FFN_GATE_1,
|
||||
MODEL_TENSOR.A_ENC_FFN_DOWN_1,
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV,
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM,
|
||||
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK,
|
||||
MODEL_TENSOR.A_MMPROJ,
|
||||
MODEL_TENSOR.A_MMPROJ_FC,
|
||||
MODEL_TENSOR.A_MM_NORM_PRE,
|
||||
MODEL_TENSOR.A_MM_NORM_MID,
|
||||
MODEL_TENSOR.A_MM_CODE_EMBD,
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q,
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_K,
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_V,
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT,
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE,
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_UP,
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN,
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN1,
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN2,
|
||||
MODEL_TENSOR.A_MM_LOCAL_NORM,
|
||||
MODEL_TENSOR.A_ENC_NORM_CONV,
|
||||
MODEL_TENSOR.A_ENC_LINEAR_POS,
|
||||
MODEL_TENSOR.A_ENC_POS_BIAS_U,
|
||||
|
|
@ -1750,6 +1819,10 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.A_ENC_CONV_NORM,
|
||||
MODEL_TENSOR.A_ENC_CONV_PW1,
|
||||
MODEL_TENSOR.A_ENC_CONV_PW2,
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_MEAN,
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_VAR,
|
||||
MODEL_TENSOR.A_ENC_MEL_FILTERS,
|
||||
MODEL_TENSOR.A_ENC_WINDOW,
|
||||
MODEL_TENSOR.A_MM_INP_PROJ,
|
||||
MODEL_TENSOR.A_MM_SOFT_EMB_NORM,
|
||||
MODEL_TENSOR.A_MM_EMBEDDING,
|
||||
|
|
@ -4291,6 +4364,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
MODEL_TENSOR.FC,
|
||||
MODEL_TENSOR.ENC_OUTPUT_NORM,
|
||||
MODEL_TENSOR.D2T,
|
||||
],
|
||||
MODEL_ARCH.DFLASH: [
|
||||
|
|
@ -4308,6 +4382,10 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_UP,
|
||||
MODEL_TENSOR.FC,
|
||||
MODEL_TENSOR.ENC_OUTPUT_NORM,
|
||||
# optional DSpark heads
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W1,
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W2,
|
||||
MODEL_TENSOR.DSPARK_CONF_PROJ,
|
||||
],
|
||||
MODEL_ARCH.MISTRAL4: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
|
|
@ -4505,7 +4583,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
],
|
||||
# TODO
|
||||
MODEL_ARCH.NANBEIGE: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
}
|
||||
|
||||
# tensors that will not be serialized
|
||||
|
|
@ -4572,6 +4665,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
],
|
||||
MODEL_ARCH.NANBEIGE: [
|
||||
MODEL_TENSOR.ROPE_FREQS,
|
||||
MODEL_TENSOR.ATTN_ROT_EMBD,
|
||||
],
|
||||
}
|
||||
|
||||
#
|
||||
|
|
@ -4777,10 +4874,12 @@ class VisionProjectorType:
|
|||
YOUTUVL = "youtuvl"
|
||||
NEMOTRON_V2_VL = "nemotron_v2_vl"
|
||||
HUNYUANVL = "hunyuanvl"
|
||||
PARAKEET = "parakeet" # audio
|
||||
MINIMAXM3 = "minimax_m3"
|
||||
MINICPMV4_6 = "minicpmv4_6"
|
||||
GRANITE_SPEECH = "granite_speech" # audio
|
||||
MIMOVL = "mimovl"
|
||||
MIMO_AUDIO = "mimo_audio"
|
||||
GRANITE4_VISION = "granite4_vision"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -908,6 +908,12 @@ class GGUFWriter:
|
|||
def add_token_shift_count(self, count: int) -> None:
|
||||
self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count)
|
||||
|
||||
def add_num_loops(self, count: int) -> None:
|
||||
self.add_uint32(Keys.LLM.NUM_LOOPS.format(arch=self.arch), count)
|
||||
|
||||
def add_skip_loop_final_norm(self, value: bool) -> None:
|
||||
self.add_bool(Keys.LLM.SKIP_LOOP_FINAL_NORM.format(arch=self.arch), value)
|
||||
|
||||
def add_interleave_moe_layer_step(self, value: int) -> None:
|
||||
self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value)
|
||||
|
||||
|
|
@ -965,6 +971,9 @@ class GGUFWriter:
|
|||
def add_norm_before_residual(self, value: bool) -> None:
|
||||
self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value)
|
||||
|
||||
def add_norm_before_fc(self, value: bool) -> None:
|
||||
self.add_bool(Keys.LLM.NORM_BEFORE_FC.format(arch=self.arch), value)
|
||||
|
||||
def add_attention_output_group_count(self, count: int) -> None:
|
||||
self.add_uint32(Keys.Attention.OUTPUT_GROUP_COUNT.format(arch=self.arch), count)
|
||||
|
||||
|
|
@ -1344,9 +1353,30 @@ class GGUFWriter:
|
|||
def add_audio_num_mel_bins(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.NUM_MEL_BINS, value)
|
||||
|
||||
def add_audio_rvq_num_quantizers(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.RVQ_NUM_QUANTIZERS, value)
|
||||
|
||||
def add_audio_rvq_codebook_size(self, values: Sequence[int]) -> None:
|
||||
self.add_array(Keys.ClipAudio.RVQ_CODEBOOK_SIZE, values)
|
||||
|
||||
def add_audio_wa_pattern_mode(self, modes: Sequence[int]) -> None:
|
||||
self.add_array(Keys.ClipAudio.WA_PATTERN_MODE, modes)
|
||||
|
||||
def add_audio_window_size(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.WINDOW_SIZE, value)
|
||||
|
||||
def add_audio_local_block_count(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.LOCAL_BLOCK_COUNT, value)
|
||||
|
||||
def add_audio_local_group_size(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.LOCAL_GROUP_SIZE, value)
|
||||
|
||||
def add_audio_stack_factor(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value)
|
||||
|
||||
def add_audio_subsampling_factor(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.SUBSAMPLING_FACTOR, value)
|
||||
|
||||
def add_audio_chunk_size(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipAudio.CHUNK_SIZE, value)
|
||||
|
||||
|
|
|
|||
|
|
@ -1304,6 +1304,18 @@ class TensorNameMap:
|
|||
"model.fc", # dflash
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W1: (
|
||||
"model.markov_head.markov_w1", # dspark
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DSPARK_MARKOV_W2: (
|
||||
"model.markov_head.markov_w2", # dspark
|
||||
),
|
||||
|
||||
MODEL_TENSOR.DSPARK_CONF_PROJ: (
|
||||
"model.confidence_head.proj", # dspark
|
||||
),
|
||||
|
||||
MODEL_TENSOR.CLS: (
|
||||
"classifier", # jina
|
||||
"classifier.dense", # roberta
|
||||
|
|
@ -2095,6 +2107,8 @@ class TensorNameMap:
|
|||
"conformer.pre_encode.conv.{bid}", # lfm2
|
||||
"model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n
|
||||
"conformer.subsample_conv_projection.layer{bid}.conv", # gemma4
|
||||
"sound_encoder.encoder.subsampling.layers.{bid}", # parakeet
|
||||
"encoder.conv{bid}", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV1D_NORM: (
|
||||
|
|
@ -2119,6 +2133,7 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.A_POST_NORM: (
|
||||
"audio_tower.layer_norm", # ultravox
|
||||
"audio_tower.ln_post", # qwen2omni
|
||||
"encoder.layer_norm", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_ATTN_Q: (
|
||||
|
|
@ -2126,7 +2141,9 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.self_attn.linear_q", # lfm2
|
||||
"conformer.layers.{bid}.attention.attn.q_proj", # gemma3n
|
||||
"conformer.layers.{bid}.self_attn.q_proj", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.q_proj", # parakeet
|
||||
"encoder.layers.{bid}.attn.to_q", # granite_speech
|
||||
"encoder.layers.{bid}.self_attn.q_proj", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_ATTN_K: (
|
||||
|
|
@ -2134,7 +2151,9 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.self_attn.linear_k", # lfm2
|
||||
"conformer.layers.{bid}.attention.attn.k_proj", # gemma3n
|
||||
"conformer.layers.{bid}.self_attn.k_proj", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.k_proj", # parakeet
|
||||
"encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv)
|
||||
"encoder.layers.{bid}.self_attn.k_proj", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_ATTN_V: (
|
||||
|
|
@ -2142,7 +2161,9 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.self_attn.linear_v", # lfm2
|
||||
"conformer.layers.{bid}.attention.attn.v_proj", # gemma3n
|
||||
"conformer.layers.{bid}.self_attn.v_proj", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.v_proj", # parakeet
|
||||
"encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv)
|
||||
"encoder.layers.{bid}.self_attn.v_proj", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_ATTN_K_REL: (
|
||||
|
|
@ -2170,7 +2191,9 @@ class TensorNameMap:
|
|||
"audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox
|
||||
"conformer.layers.{bid}.norm_self_att", # lfm2
|
||||
"conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.norm_self_att", # parakeet
|
||||
"encoder.layers.{bid}.attn.pre_norm", # granite_speech
|
||||
"encoder.layers.{bid}.self_attn_layer_norm", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_OUTPUT: (
|
||||
|
|
@ -2178,20 +2201,25 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.self_attn.linear_out", # lfm2
|
||||
"conformer.layers.{bid}.attention.post", # gemma3n
|
||||
"conformer.layers.{bid}.self_attn.post", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.o_proj", # parakeet
|
||||
"encoder.layers.{bid}.attn.to_out", # granite_speech
|
||||
"encoder.layers.{bid}.self_attn.out_proj", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_OUTPUT_NORM: (
|
||||
"audio_tower.layers.{bid}.final_layer_norm", # ultravox
|
||||
"conformer.layers.{bid}.norm_out", # lfm2
|
||||
"conformer.layers.{bid}.attention.post_norm", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.norm_out", # parakeet
|
||||
"encoder.layers.{bid}.post_norm", # granite_speech
|
||||
"encoder.layers.{bid}.final_layer_norm", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_FFN_NORM: (
|
||||
"conformer.layers.{bid}.norm_feed_forward1", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward1.pre_layer_norm", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.norm_feed_forward1", # parakeet
|
||||
"encoder.layers.{bid}.ff1.pre_norm", # granite_speech
|
||||
),
|
||||
|
||||
|
|
@ -2209,7 +2237,9 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.feed_forward1.linear1", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.feed_forward1.linear1", # parakeet
|
||||
"encoder.layers.{bid}.ff1.up_proj", # granite_speech
|
||||
"encoder.layers.{bid}.fc1", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_FFN_GATE: (),
|
||||
|
|
@ -2219,13 +2249,16 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.feed_forward1.linear2", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.feed_forward1.linear2", # parakeet
|
||||
"encoder.layers.{bid}.ff1.down_proj", # granite_speech
|
||||
"encoder.layers.{bid}.fc2", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_FFN_UP_1: (
|
||||
"conformer.layers.{bid}.feed_forward2.linear1", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward2.ffw_layer_1", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.feed_forward2.linear1", # parakeet
|
||||
"encoder.layers.{bid}.ff2.up_proj", # granite_speech
|
||||
),
|
||||
|
||||
|
|
@ -2233,6 +2266,7 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.feed_forward2.linear2", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward2.ffw_layer_2", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.feed_forward2.linear2", # parakeet
|
||||
"encoder.layers.{bid}.ff2.down_proj", # granite_speech
|
||||
),
|
||||
|
||||
|
|
@ -2240,9 +2274,23 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.norm_feed_forward2", # lfm2
|
||||
"conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward2.pre_layer_norm", # gemma4
|
||||
"sound_encoder.encoder.layers.{bid}.norm_feed_forward2", # parakeet
|
||||
"encoder.layers.{bid}.ff2.pre_norm", # granite_speech
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV: (
|
||||
"encoder.down_sample_layer.0", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM: (
|
||||
"encoder.down_sample_norm", # mimo-audio-tokenizer
|
||||
),
|
||||
|
||||
# note: the raw per-quantizer "encoder.quantizer.vq.layers.{i}._codebook.embed"
|
||||
# tensors are merged (padded + stacked, like MoE experts) into this single 3D
|
||||
# tensor in conversion code, so no raw-name mapping is registered here.
|
||||
MODEL_TENSOR.A_ENC_RVQ_CODEBOOK: (),
|
||||
|
||||
MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: (
|
||||
"conformer.layers.{bid}.ffw_layer_end.post_layer_norm", # gemma3n
|
||||
"conformer.layers.{bid}.feed_forward2.post_layer_norm", # gemma4
|
||||
|
|
@ -2255,20 +2303,24 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.A_ENC_LINEAR_POS: (
|
||||
"conformer.layers.{bid}.self_attn.linear_pos", # lfm2
|
||||
"conformer.layers.{bid}.attention.attn.relative_position_embedding.pos_proj", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.relative_k_proj", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_POS_BIAS_U: (
|
||||
"conformer.layers.{bid}.self_attn.pos_bias_u", # lfm2
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.bias_u", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_POS_BIAS_V: (
|
||||
"conformer.layers.{bid}.self_attn.pos_bias_v", # lfm2
|
||||
"sound_encoder.encoder.layers.{bid}.self_attn.bias_v", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_OUT: (
|
||||
"conformer.pre_encode.out", # lfm2
|
||||
"model.audio_tower.subsample_conv_projection.input_proj_linear", # gemma3n (note: it should be A_ENC_INP_PROJ, this is a mistake; it should be corrected in C++ code when it's supported)
|
||||
"conformer.output_proj", # gemma4
|
||||
"sound_encoder.encoder.subsampling.linear", # parakeet
|
||||
),
|
||||
|
||||
# note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors
|
||||
|
|
@ -2278,6 +2330,7 @@ class TensorNameMap:
|
|||
"audio.multi_modal_projector.linear_{bid}", # ultravox, meralion
|
||||
"audio_adapter.model.{bid}", # lfm2
|
||||
"audio_tower.proj{bid}", # qwen3omni
|
||||
"sound_projection.linear{bid}", # parakeet (linear1, linear2)
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_MMPROJ_FC: (
|
||||
|
|
@ -2288,39 +2341,89 @@ class TensorNameMap:
|
|||
|
||||
MODEL_TENSOR.A_MM_NORM_PRE: (
|
||||
"audio.multi_modal_projector.ln_pre", # ultravox
|
||||
"sound_projection.norm", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_MM_NORM_MID: (
|
||||
"audio.multi_modal_projector.ln_mid", # ultravox
|
||||
),
|
||||
|
||||
# note: the raw per-channel "speech_embeddings.{i}" tensors are merged
|
||||
# (stacked, like MoE experts) into this single 3D tensor in conversion
|
||||
# code, so no raw-name mapping is registered here.
|
||||
MODEL_TENSOR.A_MM_CODE_EMBD: (),
|
||||
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_Q: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.q_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_K: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.k_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_V: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.v_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_ATTN_OUT: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.self_attn.o_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_GATE: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.mlp.gate_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_UP: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.mlp.up_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_FFN_DOWN: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.mlp.down_proj", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN1: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.input_layernorm", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_LN2: (
|
||||
"audio_encoder.input_local_transformer.layers.{bid}.post_attention_layernorm", # mimo-v2.5
|
||||
),
|
||||
MODEL_TENSOR.A_MM_LOCAL_NORM: (
|
||||
"audio_encoder.input_local_transformer.norm", # mimo-v2.5
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_DW: (
|
||||
"conformer.layers.{bid}.conv.depthwise_conv", # lfm2
|
||||
"conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.conv.depthwise_conv", # parakeet
|
||||
"encoder.layers.{bid}.conv.depth_conv.conv", # granite_speech
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM: (
|
||||
"conformer.layers.{bid}.conv.batch_norm", # lfm2
|
||||
"conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.conv.norm", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_MEAN: (
|
||||
"sound_encoder.encoder.layers.{bid}.conv.norm.running_mean", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_NORM_VAR: (
|
||||
"sound_encoder.encoder.layers.{bid}.conv.norm.running_var", # parakeet
|
||||
"encoder.layers.{bid}.conv.batch_norm", # granite_speech
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_PW1: (
|
||||
"conformer.layers.{bid}.conv.pointwise_conv1", # lfm2
|
||||
"conformer.layers.{bid}.lconv1d.linear_start", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.conv.pointwise_conv1", # parakeet
|
||||
"encoder.layers.{bid}.conv.up_conv", # granite_speech
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_CONV_PW2: (
|
||||
"conformer.layers.{bid}.conv.pointwise_conv2", # lfm2
|
||||
"conformer.layers.{bid}.lconv1d.linear_end", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.conv.pointwise_conv2", # parakeet
|
||||
"encoder.layers.{bid}.conv.down_conv", # granite_speech
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_NORM_CONV: (
|
||||
"conformer.layers.{bid}.norm_conv", # lfm2
|
||||
"conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n
|
||||
"sound_encoder.encoder.layers.{bid}.norm_conv", # parakeet
|
||||
"encoder.layers.{bid}.conv.norm", # granite_speech
|
||||
),
|
||||
|
||||
|
|
@ -2332,6 +2435,14 @@ class TensorNameMap:
|
|||
"conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma4
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_MEL_FILTERS: (
|
||||
"sound_encoder.encoder.feature_extractor.featurizer.fb", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_ENC_WINDOW: (
|
||||
"sound_encoder.encoder.feature_extractor.featurizer.window", # parakeet
|
||||
),
|
||||
|
||||
MODEL_TENSOR.A_MM_EMBEDDING: (
|
||||
"model.embed_audio.embedding", # gemma3n
|
||||
),
|
||||
|
|
|
|||
|
|
@ -143,6 +143,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
|
||||
{ LLM_ARCH_TALKIE, "talkie" },
|
||||
{ LLM_ARCH_MELLUM, "mellum" },
|
||||
{ LLM_ARCH_NANBEIGE, "nanbeige" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
|
||||
|
|
@ -221,6 +222,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" },
|
||||
{ LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" },
|
||||
{ LLM_KV_FULL_ATTENTION_INTERVAL, "%s.full_attention_interval" },
|
||||
{ LLM_KV_NUM_LOOPS, "%s.num_loops" },
|
||||
{ LLM_KV_SKIP_LOOP_FINAL_NORM, "%s.skip_loop_final_norm" },
|
||||
|
||||
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
|
||||
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
|
||||
|
|
@ -314,6 +317,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_TARGET_LAYERS, "%s.target_layers" },
|
||||
{ LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" },
|
||||
{ LLM_KV_NORM_BEFORE_RESIDUAL, "%s.norm_before_residual" },
|
||||
{ LLM_KV_NORM_BEFORE_FC, "%s.norm_before_fc" },
|
||||
|
||||
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
|
||||
// sentence-transformers dense modules feature dims
|
||||
|
|
@ -612,6 +616,9 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
|
|||
{ LLM_TENSOR_MASKED_EMBD_ORDERING, "masked_embd_ordering" },
|
||||
{ LLM_TENSOR_FC, "fc" },
|
||||
{ LLM_TENSOR_D2T, "d2t" },
|
||||
{ LLM_TENSOR_DSPARK_MARKOV_W1, "markov_w1" },
|
||||
{ LLM_TENSOR_DSPARK_MARKOV_W2, "markov_w2" },
|
||||
{ LLM_TENSOR_DSPARK_CONF_PROJ, "conf_proj" },
|
||||
};
|
||||
|
||||
// declare information about the model weight tensors:
|
||||
|
|
@ -866,6 +873,10 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
|
|||
// eagle3
|
||||
{LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
|
||||
// dspark
|
||||
{LLM_TENSOR_DSPARK_MARKOV_W1, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
|
||||
{LLM_TENSOR_DSPARK_MARKOV_W2, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
|
||||
{LLM_TENSOR_DSPARK_CONF_PROJ, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
|
||||
};
|
||||
|
||||
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
|
||||
|
|
|
|||
|
|
@ -148,6 +148,7 @@ enum llm_arch {
|
|||
LLM_ARCH_EAGLE3,
|
||||
LLM_ARCH_MINIMAX_M3,
|
||||
LLM_ARCH_DFLASH,
|
||||
LLM_ARCH_NANBEIGE,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -226,6 +227,8 @@ enum llm_kv {
|
|||
LLM_KV_TOKEN_SHIFT_COUNT,
|
||||
LLM_KV_INTERLEAVE_MOE_LAYER_STEP,
|
||||
LLM_KV_FULL_ATTENTION_INTERVAL,
|
||||
LLM_KV_NUM_LOOPS,
|
||||
LLM_KV_SKIP_LOOP_FINAL_NORM,
|
||||
|
||||
LLM_KV_ATTENTION_HEAD_COUNT,
|
||||
LLM_KV_ATTENTION_HEAD_COUNT_KV,
|
||||
|
|
@ -360,6 +363,7 @@ enum llm_kv {
|
|||
LLM_KV_TARGET_LAYERS,
|
||||
LLM_KV_TARGET_HIDDEN_SIZE,
|
||||
LLM_KV_NORM_BEFORE_RESIDUAL,
|
||||
LLM_KV_NORM_BEFORE_FC,
|
||||
|
||||
LLM_KV_SHORTCONV_L_CACHE,
|
||||
|
||||
|
|
@ -620,6 +624,9 @@ enum llm_tensor {
|
|||
LLM_TENSOR_MASKED_EMBD_ORDERING,
|
||||
LLM_TENSOR_FC,
|
||||
LLM_TENSOR_D2T,
|
||||
LLM_TENSOR_DSPARK_MARKOV_W1,
|
||||
LLM_TENSOR_DSPARK_MARKOV_W2,
|
||||
LLM_TENSOR_DSPARK_CONF_PROJ,
|
||||
};
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2349,6 +2349,7 @@ uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
|
|||
model.arch == LLM_ARCH_QWEN35 ||
|
||||
model.arch == LLM_ARCH_QWEN35MOE ||
|
||||
model.arch == LLM_ARCH_DEEPSEEK4 ||
|
||||
model.arch == LLM_ARCH_NANBEIGE ||
|
||||
model.arch == LLM_ARCH_MINIMAX_M3) {
|
||||
return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
|
||||
}
|
||||
|
|
|
|||
|
|
@ -47,6 +47,7 @@ struct llama_hparams {
|
|||
bool use_par_res;
|
||||
bool swin_norm;
|
||||
bool norm_before_residual = false;
|
||||
bool norm_before_fc = false;
|
||||
|
||||
uint32_t n_ctx_train; // context size the model was trained on
|
||||
uint32_t n_embd;
|
||||
|
|
|
|||
|
|
@ -126,6 +126,7 @@
|
|||
#include "models/mistral4.cpp"
|
||||
#include "models/modern-bert.cpp"
|
||||
#include "models/mpt.cpp"
|
||||
#include "models/nanbeige.cpp"
|
||||
#include "models/nemotron-h-moe.cpp"
|
||||
#include "models/nemotron-h.cpp"
|
||||
#include "models/nemotron.cpp"
|
||||
|
|
@ -226,6 +227,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
|
|||
return new llama_model_stablelm(params);
|
||||
case LLM_ARCH_MELLUM:
|
||||
return new llama_model_mellum(params);
|
||||
case LLM_ARCH_NANBEIGE:
|
||||
return new llama_model_nanbeige(params);
|
||||
case LLM_ARCH_QWEN:
|
||||
return new llama_model_qwen(params);
|
||||
case LLM_ARCH_QWEN2:
|
||||
|
|
@ -957,6 +960,7 @@ const char * llm_type_name(llm_type type) {
|
|||
case LLM_TYPE_100B_A6B: return "100B.A6B";
|
||||
case LLM_TYPE_102B_A12B: return "102B.A12B";
|
||||
case LLM_TYPE_106B_A12B: return "106B.A12B";
|
||||
case LLM_TYPE_118B_A8B: return "118B.A8B";
|
||||
case LLM_TYPE_120B_A12B: return "120B.A12B";
|
||||
case LLM_TYPE_122B_A10B: return "122B.A10B";
|
||||
case LLM_TYPE_196B_A11B: return "196B.A11B";
|
||||
|
|
@ -2210,7 +2214,6 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
res = nullptr;
|
||||
} break;
|
||||
case LLM_ARCH_DEEPSEEK32:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
{
|
||||
res = new llama_kv_cache_dsa(
|
||||
*this,
|
||||
|
|
@ -2227,6 +2230,56 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
nullptr,
|
||||
nullptr);
|
||||
} break;
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
{
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP && hparams.n_layer_nextn > 0) {
|
||||
// The NextN/MTP draft head runs dense MLA (no DSA indexer), so the
|
||||
// MTP context uses a plain attention KV cache holding only the
|
||||
// nextn layer(s) - same pattern as the hybrid Qwen3.5 MTP context.
|
||||
llama_kv_cache::layer_filter_cb filter =
|
||||
[&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
|
||||
res = new llama_kv_cache(
|
||||
*this,
|
||||
hparams,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
nullptr,
|
||||
filter,
|
||||
nullptr,
|
||||
nullptr);
|
||||
} else {
|
||||
// Main context: DSA cache for the trunk layers only - the nextn
|
||||
// layer(s) are never attended by the trunk graph.
|
||||
llama_kv_cache::layer_filter_cb filter = nullptr;
|
||||
if (hparams.n_layer_nextn > 0) {
|
||||
filter = [&](uint32_t il) { return il < hparams.n_layer(); };
|
||||
}
|
||||
|
||||
res = new llama_kv_cache_dsa(
|
||||
*this,
|
||||
params.type_k,
|
||||
params.type_v,
|
||||
!cparams.flash_attn,
|
||||
cparams.offload_kqv,
|
||||
cparams.kv_unified,
|
||||
cparams.n_ctx_seq,
|
||||
cparams.n_seq_max,
|
||||
1,
|
||||
hparams.n_swa,
|
||||
hparams.swa_type,
|
||||
filter,
|
||||
nullptr);
|
||||
}
|
||||
} break;
|
||||
// Models that need standard caching should rely on recurrent/hybrid
|
||||
// checks
|
||||
default:
|
||||
|
|
@ -2332,7 +2385,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
|
|||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
}
|
||||
|
||||
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3) && hparams.n_layer_nextn > 0) {
|
||||
if ((arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_HY_V3 || arch == LLM_ARCH_GLM_DSA) && hparams.n_layer_nextn > 0) {
|
||||
if (params.ctx_type == LLAMA_CONTEXT_TYPE_MTP) {
|
||||
filter = [&](uint32_t il) { return il >= hparams.n_layer(); };
|
||||
} else {
|
||||
|
|
@ -2632,6 +2685,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_LLAMA_EMBED:
|
||||
case LLM_ARCH_MAINCODER:
|
||||
case LLM_ARCH_GLM_DSA:
|
||||
case LLM_ARCH_NANBEIGE:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
|
|
|||
|
|
@ -130,6 +130,7 @@ enum llm_type {
|
|||
LLM_TYPE_100B_A6B,
|
||||
LLM_TYPE_102B_A12B, // Solar-Open
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
LLM_TYPE_118B_A8B, // Laguna-S-2
|
||||
LLM_TYPE_120B_A12B, // Nemotron 3 Super
|
||||
LLM_TYPE_122B_A10B, // Qwen3.5
|
||||
LLM_TYPE_196B_A11B, // Step3.5-Flash
|
||||
|
|
@ -606,6 +607,12 @@ struct llama_model {
|
|||
struct ggml_tensor * fc = nullptr; // feature fusion layer
|
||||
struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping
|
||||
|
||||
// dspark
|
||||
struct ggml_tensor * dspark_markov_w1 = nullptr;
|
||||
struct ggml_tensor * dspark_markov_w2 = nullptr;
|
||||
struct ggml_tensor * dspark_conf_proj = nullptr;
|
||||
struct ggml_tensor * dspark_conf_proj_b = nullptr;
|
||||
|
||||
// unified vector to store target-model extracted layer ids in eagle3, dflash, etc.
|
||||
std::vector<int32_t> target_layer_ids;
|
||||
|
||||
|
|
|
|||
|
|
@ -359,6 +359,10 @@ static bool tensor_allows_quantization(const llama_model_quantize_params * param
|
|||
quantize &= name.find(".patch_embd") == std::string::npos;
|
||||
quantize &= name.find(".patch_merger") == std::string::npos;
|
||||
|
||||
// audio codebook
|
||||
quantize &= name.find("a.rvq.codebook") == std::string::npos;
|
||||
quantize &= name.find("mm.a.code_embd") == std::string::npos;
|
||||
|
||||
return quantize;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -993,7 +993,9 @@ static void llama_sampler_greedy_backend_apply(
|
|||
GGML_UNUSED(gf);
|
||||
GGML_UNUSED(smpl);
|
||||
|
||||
struct ggml_tensor * curl = ggml_argmax(ctx, data->logits);
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
struct ggml_tensor * curl = ggml_argmax(ctx, logits);
|
||||
ggml_set_name(curl, "greedy_argmax");
|
||||
|
||||
data->sampled = curl;
|
||||
|
|
@ -1158,7 +1160,10 @@ static void llama_sampler_dist_backend_apply(
|
|||
ggml_set_name (sctx->inp_uniform, "uniform");
|
||||
ggml_set_input(sctx->inp_uniform);
|
||||
|
||||
struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
|
||||
// flatten
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
struct ggml_tensor * probs = ggml_soft_max(ctx, logits);
|
||||
ggml_set_name(probs, "dist_probs");
|
||||
|
||||
struct ggml_tensor * cumsum = ggml_cumsum(ctx, probs);
|
||||
|
|
@ -1289,22 +1294,22 @@ static void llama_sampler_top_k_backend_apply(
|
|||
struct llama_sampler_data * data) {
|
||||
auto * sctx = (llama_sampler_top_k *) smpl->ctx;
|
||||
|
||||
struct ggml_tensor * top_k = ggml_top_k(ctx, data->logits, sctx->k);
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
struct ggml_tensor * top_k = ggml_top_k(ctx, logits, sctx->k);
|
||||
ggml_set_name(top_k, "top_k");
|
||||
|
||||
if (data->candidates) {
|
||||
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]);
|
||||
data->candidates = ggml_get_rows(ctx, candidates_rows, top_k);
|
||||
data->candidates = ggml_reshape_1d(ctx, data->candidates, sctx->k);
|
||||
ggml_set_name(data->candidates, "top_k_candidates");
|
||||
} else {
|
||||
data->candidates = top_k;
|
||||
}
|
||||
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
|
||||
struct ggml_tensor * top_k_rows = ggml_get_rows(ctx, logits_rows, top_k);
|
||||
data->logits = ggml_reshape_1d(ctx, top_k_rows, sctx->k);
|
||||
ggml_set_name(top_k_rows, "top_k_rows");
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]);
|
||||
data->logits = ggml_get_rows(ctx, logits_rows, top_k);
|
||||
ggml_set_name(data->logits, "top_k_rows");
|
||||
|
||||
GGML_UNUSED(gf);
|
||||
}
|
||||
|
|
@ -1435,21 +1440,25 @@ static void llama_sampler_top_p_backend_apply(
|
|||
struct llama_sampler_data * data) {
|
||||
auto * sctx = (llama_sampler_top_p *) smpl->ctx;
|
||||
|
||||
// flatten
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
auto ggml_sort = [ctx](struct ggml_tensor * a, struct ggml_tensor * b) {
|
||||
GGML_ASSERT(ggml_nrows(a) == 1);
|
||||
struct ggml_tensor * a_reshaped = ggml_reshape_2d(ctx, a, 1, a->ne[0]);
|
||||
struct ggml_tensor * a_sorted = ggml_get_rows(ctx, a_reshaped, b);
|
||||
return ggml_reshape_1d(ctx, a_sorted, a->ne[0]);
|
||||
return a_sorted;
|
||||
};
|
||||
|
||||
// Get the sorted logits in descending order.
|
||||
struct ggml_tensor * sorted_idx = ggml_argsort(ctx, data->logits, GGML_SORT_ORDER_DESC);
|
||||
struct ggml_tensor * sorted_idx = ggml_argsort(ctx, logits, GGML_SORT_ORDER_DESC);
|
||||
ggml_set_name(sorted_idx, "top_p_sorted_idx");
|
||||
|
||||
// Do the sorting via reshape + get_rows
|
||||
struct ggml_tensor * sorted_logits = ggml_sort(data->logits, sorted_idx);
|
||||
struct ggml_tensor * sorted_logits = ggml_sort(logits, sorted_idx);
|
||||
ggml_set_name(sorted_logits, "top_p_sorted_logits");
|
||||
|
||||
sorted_logits = ggml_reshape_1d(ctx, sorted_logits, ggml_nelements(sorted_logits));
|
||||
struct ggml_tensor * softmax = ggml_soft_max(ctx, sorted_logits);
|
||||
ggml_set_name(softmax, "top_p_softmax");
|
||||
|
||||
|
|
@ -1626,10 +1635,12 @@ static void llama_sampler_min_p_backend_apply(
|
|||
struct llama_sampler_data * data) {
|
||||
auto * sctx = (llama_sampler_min_p *) smpl->ctx;
|
||||
|
||||
struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
struct ggml_tensor * max_idx = ggml_argmax(ctx, logits);
|
||||
ggml_set_name(max_idx, "max_idx");
|
||||
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, logits->ne[0]);
|
||||
ggml_set_name(logits_rows, "logits_rows");
|
||||
|
||||
struct ggml_tensor * max_logit = ggml_get_rows(ctx, logits_rows, max_idx);
|
||||
|
|
@ -1640,7 +1651,7 @@ static void llama_sampler_min_p_backend_apply(
|
|||
ggml_set_name(threshold, "min_p_threshold");
|
||||
|
||||
// Subtract the threshold from logits.
|
||||
struct ggml_tensor * sub = ggml_sub(ctx, data->logits, threshold);
|
||||
struct ggml_tensor * sub = ggml_sub(ctx, logits, threshold);
|
||||
|
||||
// Create a mask where logits below the threshold are 0 (discard),
|
||||
// and others are 1 (keep).
|
||||
|
|
@ -1652,7 +1663,7 @@ static void llama_sampler_min_p_backend_apply(
|
|||
struct ggml_tensor * min_p_bias = ggml_log(ctx, mask);
|
||||
ggml_set_name(min_p_bias, "min_p_bias");
|
||||
|
||||
data->logits = ggml_add(ctx, data->logits, min_p_bias);
|
||||
data->logits = ggml_add(ctx, logits, min_p_bias);
|
||||
ggml_set_name(data->logits, "min_p_logits");
|
||||
|
||||
GGML_UNUSED(gf);
|
||||
|
|
@ -1829,18 +1840,20 @@ static void llama_sampler_backend_temp_sampling(
|
|||
struct llama_sampler_data * data,
|
||||
float temp) {
|
||||
if (temp <= 0.0f) {
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
// Find the most probable token index.
|
||||
struct ggml_tensor * max_idx = ggml_argmax(ctx, data->logits);
|
||||
struct ggml_tensor * max_idx = ggml_argmax(ctx, logits);
|
||||
ggml_set_name(max_idx, "temp_max_idx");
|
||||
|
||||
if (data->candidates) {
|
||||
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, data->candidates->ne[0]);
|
||||
struct ggml_tensor * candidates_rows = ggml_reshape_2d(ctx, data->candidates, 1, ggml_nelements(data->candidates));
|
||||
data->candidates = ggml_get_rows(ctx, candidates_rows, max_idx);
|
||||
} else {
|
||||
data->candidates = max_idx;
|
||||
}
|
||||
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, data->logits, 1, data->logits->ne[0]);
|
||||
struct ggml_tensor * logits_rows = ggml_reshape_2d(ctx, logits, 1, ggml_nelements(logits));
|
||||
data->logits = ggml_get_rows(ctx, logits_rows, max_idx);
|
||||
|
||||
return;
|
||||
|
|
@ -2019,13 +2032,15 @@ static void llama_sampler_temp_ext_backend_apply(
|
|||
return;
|
||||
}
|
||||
|
||||
struct ggml_tensor * logits = ggml_reshape_1d(ctx, data->logits, ggml_nelements(data->logits));
|
||||
|
||||
// Calculate min_temp, max_temp, and max_entropy.
|
||||
const float min_temp = std::max(0.0f, sctx->temp - sctx->delta);
|
||||
const float max_temp = sctx->temp + sctx->delta;
|
||||
const float max_entropy = logf(data->logits->ne[0]);
|
||||
const float max_entropy = logf(logits->ne[0]);
|
||||
|
||||
// Calculate the probabilities.
|
||||
struct ggml_tensor * probs = ggml_soft_max(ctx, data->logits);
|
||||
struct ggml_tensor * probs = ggml_soft_max(ctx, logits);
|
||||
ggml_set_name(probs, "temp_ext_softmax_probs");
|
||||
|
||||
// Clamp probabilities to avoid log(0) which would give -inf
|
||||
|
|
@ -2063,7 +2078,7 @@ static void llama_sampler_temp_ext_backend_apply(
|
|||
ggml_set_name(dyn_temp, "temp_ext_dyn_temp");
|
||||
|
||||
// Scale the logits by the dynamic temperature
|
||||
struct ggml_tensor * scaled_logits = ggml_div(ctx, data->logits, dyn_temp);
|
||||
struct ggml_tensor * scaled_logits = ggml_div(ctx, logits, dyn_temp);
|
||||
ggml_set_name(scaled_logits, "temp_ext_scaled_logits");
|
||||
|
||||
data->logits = scaled_logits;
|
||||
|
|
|
|||
|
|
@ -37,6 +37,23 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
|
|||
|
||||
const int64_t n_embd_inp = hparams.n_embd_inp_enc();
|
||||
|
||||
// DSpark = DFlash + a semi-autoregressive Markov head and Confidence head
|
||||
//
|
||||
// TODO: only Qwen3-style backbones are supported for now; other backbones (e.g. Gemma4)
|
||||
// need their own conversion path and graph tweaks
|
||||
const struct ggml_tensor * markov_meta = ml->get_tensor_meta("markov_w1.weight");
|
||||
if (markov_meta) {
|
||||
const int64_t dspark_markov_rank = markov_meta->ne[0];
|
||||
|
||||
dspark_markov_w1 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W1, "weight"), { dspark_markov_rank, n_vocab }, 0);
|
||||
dspark_markov_w2 = create_tensor(tn(LLM_TENSOR_DSPARK_MARKOV_W2, "weight"), { dspark_markov_rank, n_vocab }, 0);
|
||||
|
||||
dspark_conf_proj = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "weight"), { n_embd + dspark_markov_rank, 1 }, 0);
|
||||
dspark_conf_proj_b = create_tensor(tn(LLM_TENSOR_DSPARK_CONF_PROJ, "bias"), { 1 }, TENSOR_NOT_REQUIRED);
|
||||
|
||||
LLAMA_LOG_INFO("%s: DFlash with DSpark markov head (rank = %lld)\n", __func__, (long long) dspark_markov_rank);
|
||||
}
|
||||
|
||||
fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), { n_embd_inp, n_embd }, 0);
|
||||
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc)
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); // decoder final norm
|
||||
|
|
@ -105,6 +122,94 @@ llama_model_dflash::graph<true>::graph(const llama_model & model, const llm_grap
|
|||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// DSpark (DFlash + Markov & Confidence head): Markov bias on the draft logits, chained per block position
|
||||
static void build_dspark_markov_head(llm_graph_context & g, const llama_model & model, ggml_tensor * tokens) {
|
||||
ggml_context * ctx0 = g.ctx0;
|
||||
auto & res = g.res;
|
||||
|
||||
ggml_tensor * w1 = model.dspark_markov_w1;
|
||||
ggml_tensor * w2 = model.dspark_markov_w2;
|
||||
GGML_ASSERT(w1 && w2 && model.dspark_conf_proj && "DSpark markov/confidence weights not loaded");
|
||||
|
||||
ggml_tensor * base = res->t_logits; // [n_vocab, n_tokens]
|
||||
const int64_t n_vocab = base->ne[0];
|
||||
const int64_t n_tok = base->ne[1];
|
||||
|
||||
const auto it = model.gguf_kv.find("dflash.block_size");
|
||||
GGML_ASSERT(it != model.gguf_kv.end() && "DSpark draft requires 'dflash.block_size' in GGUF metadata");
|
||||
const int64_t block_size = std::stoi(it->second);
|
||||
GGML_ASSERT(block_size > 0);
|
||||
|
||||
const int64_t n_blocks = g.ubatch.n_seqs_unq;
|
||||
GGML_ASSERT(n_blocks > 0 && n_tok % n_blocks == 0 && "DSpark markov head requires equal-size blocks");
|
||||
// runtime tokens per block in this ubatch (anchor + drafted positions), bounded by training block_size
|
||||
const int64_t block_drafts = n_tok / n_blocks;
|
||||
if (block_drafts > block_size) {
|
||||
return;
|
||||
}
|
||||
|
||||
// anchor (committed last) token of every block: token 0 of each block, i.e. a strided view
|
||||
const size_t token_stride = (size_t) block_drafts * tokens->nb[0];
|
||||
const size_t base_stride = (size_t) block_drafts * base->nb[1];
|
||||
|
||||
ggml_tensor * prev = ggml_view_2d(ctx0, tokens, 1, n_blocks, token_stride, 0);
|
||||
prev = ggml_cont_1d(ctx0, prev, n_blocks);
|
||||
|
||||
// confidence head input: predicts per-position acceptance
|
||||
ggml_tensor * conf_inp = res->t_embd; // [n_embd, n_tok]
|
||||
|
||||
ggml_tensor * cat = nullptr;
|
||||
ggml_tensor * cat_conf = nullptr;
|
||||
|
||||
// TODO: the in-graph chain is greedy (argmax); sampling params affect only the final
|
||||
// token pick, not the Markov conditioning path
|
||||
for (int64_t i = 0; i < block_drafts; ++i) {
|
||||
ggml_tensor * w1_prev = ggml_get_rows(ctx0, w1, prev); // [R, n_blocks]
|
||||
ggml_tensor * bias = ggml_mul_mat(ctx0, w2, w1_prev); // [n_vocab, n_blocks]
|
||||
|
||||
// position i of every block: strided view [n_vocab, n_blocks]
|
||||
ggml_tensor * base_i = ggml_view_2d(ctx0, base, n_vocab, n_blocks, base_stride, i*base->nb[1]);
|
||||
ggml_tensor * col = ggml_add(ctx0, base_i, bias);
|
||||
|
||||
cat = cat ? ggml_concat(ctx0, cat, col, 1) : col;
|
||||
|
||||
// conf(i) = sigmoid(conf_proj . [conf_inp(i); markov_w1[prev(i)]] + b) -- [1, n_blocks]
|
||||
ggml_tensor * conf_inp_i = ggml_view_2d(ctx0, conf_inp, conf_inp->ne[0], n_blocks,
|
||||
(size_t) block_drafts * conf_inp->nb[1], i*conf_inp->nb[1]);
|
||||
ggml_tensor * feat = ggml_concat(ctx0, ggml_cont(ctx0, conf_inp_i), w1_prev, 0);
|
||||
ggml_tensor * conf = ggml_mul_mat(ctx0, model.dspark_conf_proj, feat);
|
||||
if (model.dspark_conf_proj_b) {
|
||||
conf = ggml_add(ctx0, conf, model.dspark_conf_proj_b);
|
||||
}
|
||||
conf = ggml_sigmoid(ctx0, conf);
|
||||
|
||||
cat_conf = cat_conf ? ggml_concat(ctx0, cat_conf, conf, 1) : conf;
|
||||
|
||||
if (i + 1 < block_drafts) {
|
||||
prev = ggml_argmax(ctx0, col);
|
||||
}
|
||||
}
|
||||
|
||||
// cat is position-major; restore ubatch block-major order
|
||||
ggml_tensor * out = ggml_reshape_3d(ctx0, cat, n_vocab, n_blocks, block_drafts);
|
||||
out = ggml_cont(ctx0, ggml_permute(ctx0, out, 0, 2, 1, 3)); // [n_vocab, block_drafts, n_blocks]
|
||||
out = ggml_reshape_2d(ctx0, out, n_vocab, n_tok);
|
||||
|
||||
{
|
||||
ggml_tensor * conf = ggml_reshape_3d(ctx0, cat_conf, 1, n_blocks, block_drafts);
|
||||
conf = ggml_cont(ctx0, ggml_permute(ctx0, conf, 0, 2, 1, 3));
|
||||
conf = ggml_reshape_2d(ctx0, conf, 1, n_tok);
|
||||
|
||||
// note: broadcast the [1, n_tok] confidences to n_embd-wide rows to be able to reuse `llama_get_embeddings_nextn`
|
||||
conf = ggml_repeat(ctx0, conf, res->t_embd);
|
||||
res->t_h_nextn = conf;
|
||||
ggml_build_forward_expand(g.gf, conf);
|
||||
}
|
||||
|
||||
res->t_logits = out;
|
||||
ggml_build_forward_expand(g.gf, out);
|
||||
}
|
||||
|
||||
// DFlash decoder, dual-mode by batch type:
|
||||
// * embd batch -> fused target features: project + inject K/V into the cache.
|
||||
// * token batch -> noise-block diffusion: attend over [committed, MASK...] to generate draft tokens
|
||||
|
|
@ -210,6 +315,8 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
|
|||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
|
||||
ggml_tensor * inp_tokens = inp->tokens;
|
||||
|
||||
ggml_tensor * inpL = ggml_get_rows(ctx0, tok_embd, inp->tokens);
|
||||
cb(inpL, "inp_noise_embd", -1);
|
||||
|
||||
|
|
@ -290,4 +397,9 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
|
|||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
// DSpark: bias the draft logits with the Markov head
|
||||
if (model.dspark_markov_w1) {
|
||||
build_dspark_markov_head(*this, model, inp_tokens);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -28,6 +28,10 @@ void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
|
|||
LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__);
|
||||
}
|
||||
|
||||
// eagle3 norm_before_fc (optional, default false)
|
||||
// compatible with eagle3.1 (e.g. nvidia/gpt-oss-120b-Eagle3-v3)
|
||||
ml.get_key(LLM_KV_NORM_BEFORE_FC, hparams.norm_before_fc, false);
|
||||
|
||||
type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
|
||||
|
|
@ -53,6 +57,11 @@ void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
|
|||
// Feature fusion layer: projects 3 target layers to draft hidden size
|
||||
fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0);
|
||||
|
||||
// RMSNorm on the fused target features (input to fc), only when norm_before_fc is set.
|
||||
if (hparams.norm_before_fc) {
|
||||
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd_inp}, 0);
|
||||
}
|
||||
|
||||
// Output layer (uses draft vocab size)
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED);
|
||||
|
|
@ -130,6 +139,12 @@ llama_model_eagle3::graph<true>::graph(const llama_model & model, const llm_grap
|
|||
|
||||
cur = build_inp_embd_enc();
|
||||
|
||||
// RMSNorm on the fused target features before fc
|
||||
if (hparams.norm_before_fc) {
|
||||
cur = build_norm(cur, model.output_norm_enc, NULL, LLM_NORM_RMS, -1);
|
||||
cb(cur, "enc_input_norm", -1);
|
||||
}
|
||||
|
||||
// Feature fusion layer
|
||||
cur = build_lora_mm(model.fc, cur);
|
||||
cb(cur, "fc_out", -1);
|
||||
|
|
|
|||
|
|
@ -72,15 +72,27 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
|
|||
ml.get_key_or_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl, hparams.n_layer(), false);
|
||||
|
||||
switch (hparams.n_layer()) {
|
||||
case 78: type = LLM_TYPE_744B_A40B; break;
|
||||
case 78: // GGUF with NextN/MTP metadata: n_layer() excludes the nextn layer
|
||||
case 79:
|
||||
type = LLM_TYPE_744B_A40B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
}
|
||||
|
||||
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
||||
void llama_model_glm_dsa::load_arch_tensors(llama_model_loader & ml) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
const int64_t n_expert_shared = hparams.n_expert_shared;
|
||||
|
||||
// MTP-only: the GGUF carries only the NextN/MTP block(s) (user split target/draft).
|
||||
const bool mtp_only = (hparams.n_layer_nextn > 0) && (ml.get_weight("blk.0.attn_norm.weight") == nullptr);
|
||||
// Trunk-only: the GGUF declares MTP layers in metadata but the actual MTP
|
||||
// tensors live in a separate file (or were stripped at conversion). Mark
|
||||
// MTP tensors NOT_REQUIRED so the trunk loads cleanly.
|
||||
const std::string mtp_probe = "blk." + std::to_string(n_layer) + ".nextn.eh_proj.weight";
|
||||
const bool trunk_only = (hparams.n_layer_nextn > 0) && (ml.get_weight(mtp_probe.c_str()) == nullptr);
|
||||
const int trunk_flags = mtp_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
const int mtp_flags = trunk_only ? TENSOR_NOT_REQUIRED : 0;
|
||||
|
||||
const bool is_mla = hparams.is_mla();
|
||||
if (!is_mla) {
|
||||
throw std::runtime_error("GLM_DSA architecture requires MLA");
|
||||
|
|
@ -109,12 +121,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
|||
}
|
||||
|
||||
for (int i = 0; i < n_layer_all; ++i) {
|
||||
int flags = 0;
|
||||
if (i >= n_layer) {
|
||||
// skip all tensors in the NextN layers
|
||||
// TODO @ngxson : TENSOR_NOT_REQUIRED was a hack, need to remove it later
|
||||
flags |= TENSOR_SKIP | TENSOR_NOT_REQUIRED;
|
||||
}
|
||||
// NextN/MTP layers (i >= n_layer) are full decoder blocks used by the
|
||||
// LLM_GRAPH_TYPE_DECODER_MTP draft head; load them like qwen35moe/step35/hy_v3.
|
||||
const int flags = (i >= n_layer) ? mtp_flags : trunk_flags;
|
||||
|
||||
auto & layer = layers[i];
|
||||
|
||||
|
|
@ -167,7 +176,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
|||
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
|
||||
}
|
||||
|
||||
// NextN/MTP tensors (preserved but unused) - conditionally load for last n_layer_nextn
|
||||
// NextN/MTP tensors - the NextN-specific wiring around the extra decoder block
|
||||
if (i >= n_layer) {
|
||||
layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
|
||||
layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
|
||||
|
|
@ -182,6 +191,9 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
|
|||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_glm_dsa::build_arch_graph(const llm_graph_params & params) const {
|
||||
if (params.gtype == LLM_GRAPH_TYPE_DECODER_MTP) {
|
||||
return std::make_unique<graph_mtp>(*this, params);
|
||||
}
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
|
|
@ -469,7 +481,9 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
|||
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
|
||||
}
|
||||
}
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
// when unmasked nextn embeddings are requested, t_h_nextn must keep all rows,
|
||||
// so the early output masking has to be skipped (it is applied after the final norm instead)
|
||||
if (il == n_layer - 1 && inp_out_ids && (!cparams.embeddings_nextn || cparams.embeddings_nextn_masked)) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
|
@ -532,6 +546,14 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
|||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
|
||||
// post-norm hidden state feeds the NextN/MTP draft head
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (cparams.embeddings_nextn && !cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
|
|
@ -543,3 +565,242 @@ llama_model_glm_dsa::graph::graph(const llama_model & model, const llm_graph_par
|
|||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// LLM_GRAPH_TYPE_DECODER_MTP draft head for GLM-5.2 (GLM_DSA).
|
||||
// Semantics mirror the deepseek-family NextN/MTP layer:
|
||||
// enorm(embed) + hnorm(prev_hidden) -> concat(e, h) -> eh_proj ->
|
||||
// full glm_dsa decoder block (dense MLA attention + sigmoid-gated MoE FFN
|
||||
// with shared expert, exactly as the trunk deepseek2 graph builds it) ->
|
||||
// shared_head_norm (fallback output_norm) -> shared LM head.
|
||||
// The DSA indexer is not used at runtime (same as the trunk graph).
|
||||
llama_model_glm_dsa::graph_mtp::graph_mtp(const llama_model & model, const llm_graph_params & params)
|
||||
: llm_graph_context(params) {
|
||||
GGML_ASSERT(hparams.n_layer_nextn > 0 && "GLM_DSA MTP requires n_layer_nextn > 0");
|
||||
GGML_ASSERT(hparams.n_layer_nextn == 1 && "GLM_DSA MTP currently only supports a single MTP block");
|
||||
GGML_ASSERT(hparams.is_mla() && "GLM_DSA MTP requires MLA");
|
||||
|
||||
const int il = hparams.n_layer() + cparams.nextn_layer_offset;
|
||||
GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
|
||||
cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
|
||||
"nextn_layer_offset out of range [0, n_layer_nextn)");
|
||||
const auto & layer = model.layers[il];
|
||||
|
||||
GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
|
||||
GGML_ASSERT(layer.nextn.enorm && "MTP block missing nextn.enorm");
|
||||
GGML_ASSERT(layer.nextn.hnorm && "MTP block missing nextn.hnorm");
|
||||
GGML_ASSERT(layer.ffn_gate_inp && "MTP block missing ffn_gate_inp");
|
||||
|
||||
// note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
|
||||
const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
|
||||
|
||||
const int64_t n_embd_head_qk_rope = hparams.n_rot();
|
||||
const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
|
||||
|
||||
const uint32_t kv_lora_rank = hparams.n_lora_kv;
|
||||
|
||||
// We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
|
||||
// See the deepseek2 trunk graph for the detailed explanation - this must match it EXACTLY.
|
||||
GGML_ASSERT(ext_factor >= 0.0f);
|
||||
const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
|
||||
|
||||
const float mscale = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
|
||||
const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
|
||||
|
||||
// TODO: extract in a common llm_graph_context::build_inp_embd_h()
|
||||
auto inp = std::make_unique<llm_graph_input_embd_h>(hparams.n_embd);
|
||||
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
||||
ggml_set_input(inp->tokens);
|
||||
|
||||
inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd_inp(), n_tokens);
|
||||
ggml_set_input(inp->embd);
|
||||
|
||||
ggml_tensor * tok_embd;
|
||||
if (ubatch.token) {
|
||||
ggml_tensor * tok_embd_w = layer.nextn.embed_tokens ? layer.nextn.embed_tokens : model.tok_embd;
|
||||
|
||||
tok_embd = ggml_get_rows(ctx0, tok_embd_w, inp->tokens);
|
||||
} else {
|
||||
tok_embd = inp->embd;
|
||||
}
|
||||
cb(tok_embd, "mtp_tok_embd", il);
|
||||
|
||||
inp->h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, hparams.n_embd, n_tokens);
|
||||
ggml_set_input(inp->h);
|
||||
ggml_set_name(inp->h, "mtp_h_input");
|
||||
|
||||
ggml_tensor * h_embd = inp->h;
|
||||
|
||||
res->add_input(std::move(inp));
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
// MLA with the absorption optimization uses a K-only cache (V is a view of K)
|
||||
auto * inp_attn = build_attn_inp_k();
|
||||
|
||||
ggml_tensor * h_norm = build_norm(h_embd, layer.nextn.hnorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(h_norm, "mtp_hnorm", il);
|
||||
|
||||
ggml_tensor * e_norm = build_norm(tok_embd, layer.nextn.enorm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(e_norm, "mtp_enorm", il);
|
||||
|
||||
ggml_tensor * concat = ggml_concat(ctx0, e_norm, h_norm, /*dim=*/ 0);
|
||||
cb(concat, "mtp_concat", il);
|
||||
|
||||
ggml_tensor * cur = build_lora_mm(layer.nextn.eh_proj, concat, layer.nextn.eh_proj_s);
|
||||
cb(cur, "mtp_eh_proj", il);
|
||||
|
||||
ggml_tensor * inpSA = cur;
|
||||
|
||||
cur = build_norm(cur, layer.attn_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_attn_norm", il);
|
||||
|
||||
// self-attention: dense MLA, same construction as the deepseek2 trunk graph
|
||||
{
|
||||
ggml_tensor * q = ggml_mul_mat(ctx0, layer.wq_a, cur);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
q = build_norm(q, layer.attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
q = ggml_mul_mat(ctx0, layer.wq_b, q);
|
||||
cb(q, "mtp_q", il);
|
||||
|
||||
// split into {n_embd_head_qk_nope, n_head, n_tokens}
|
||||
ggml_tensor * q_nope =
|
||||
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
|
||||
cb(q_nope, "mtp_q_nope", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, n_head, n_tokens}
|
||||
ggml_tensor * q_pe = ggml_view_3d(
|
||||
ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
|
||||
ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
|
||||
cb(q_pe, "mtp_q_pe", il);
|
||||
|
||||
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, layer.wkv_a_mqa, cur);
|
||||
cb(kv_cmpr_pe, "mtp_kv_cmpr_pe", il);
|
||||
|
||||
// split into {kv_lora_rank, n_tokens}
|
||||
ggml_tensor * kv_cmpr =
|
||||
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr", il);
|
||||
|
||||
// and {n_embd_head_qk_rope, 1, n_tokens}
|
||||
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
||||
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
||||
cb(k_pe, "mtp_k_pe", il);
|
||||
|
||||
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(q_pe, "mtp_q_pe", il);
|
||||
|
||||
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
cb(k_pe, "mtp_k_pe", il);
|
||||
|
||||
kv_cmpr = build_norm(kv_cmpr, layer.attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr", il);
|
||||
|
||||
// {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
||||
cb(q_nope, "mtp_q_nope_perm", il);
|
||||
|
||||
// {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
|
||||
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, layer.wk_b, q_nope);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed", il);
|
||||
|
||||
// {kv_lora_rank, n_head, n_tokens}
|
||||
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
||||
cb(q_nope_absorbed, "mtp_q_nope_absorbed_perm", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
|
||||
// note: rope must go first for in-place context shifting in build_rope_shift()
|
||||
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
||||
cb(Qcur, "mtp_Qcur", il);
|
||||
|
||||
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
||||
cb(kv_cmpr, "mtp_kv_cmpr_reshape", il);
|
||||
|
||||
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
||||
cb(Kcur, "mtp_Kcur", il);
|
||||
|
||||
// {kv_lora_rank, 1, n_tokens}
|
||||
ggml_tensor * Vcur = kv_cmpr;
|
||||
cb(Vcur, "mtp_Vcur", il);
|
||||
|
||||
// note: MLA with the absorption optimization converts into MQA (ie: GQA with 1 group)
|
||||
cur = build_attn(inp_attn,
|
||||
layer.wo, NULL, layer.wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale, il);
|
||||
cb(cur, "mtp_attn_out", il);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "mtp_ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, layer.ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "mtp_ffn_norm", il);
|
||||
|
||||
// MoE FFN with shared expert - same construction as the deepseek2 trunk graph
|
||||
ggml_tensor * moe_out = build_moe_ffn(cur,
|
||||
layer.ffn_gate_inp,
|
||||
layer.ffn_up_exps,
|
||||
layer.ffn_gate_exps,
|
||||
layer.ffn_down_exps,
|
||||
layer.ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il,
|
||||
nullptr,
|
||||
layer.ffn_gate_up_exps,
|
||||
layer.ffn_up_exps_s,
|
||||
layer.ffn_gate_exps_s,
|
||||
layer.ffn_down_exps_s);
|
||||
cb(moe_out, "mtp_ffn_moe_out", il);
|
||||
|
||||
// FFN shared expert
|
||||
ggml_tensor * ffn_shexp =
|
||||
build_ffn(cur,
|
||||
layer.ffn_up_shexp, NULL, layer.ffn_up_shexp_s,
|
||||
layer.ffn_gate_shexp, NULL, layer.ffn_gate_shexp_s,
|
||||
layer.ffn_down_shexp, NULL, layer.ffn_down_shexp_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "mtp_ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "mtp_ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "mtp_post_ffn", il);
|
||||
|
||||
// shared_head_norm applied after the decoder block, before the shared LM head.
|
||||
// The post-norm hidden state seeds the next MTP step.
|
||||
ggml_tensor * head_norm_w = layer.nextn.shared_head_norm
|
||||
? layer.nextn.shared_head_norm
|
||||
: model.output_norm;
|
||||
GGML_ASSERT(head_norm_w && "GLM_DSA MTP: missing both nextn.shared_head_norm and output_norm");
|
||||
cur = build_norm(cur, head_norm_w, nullptr, LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "h_nextn", -1);
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
cb(cur, "mtp_shared_head_norm", -1);
|
||||
|
||||
ggml_tensor * head_w = layer.nextn.shared_head_head ? layer.nextn.shared_head_head : model.output;
|
||||
ggml_tensor * head_s = layer.nextn.shared_head_head ? layer.nextn.shared_head_head_s : model.output_s;
|
||||
GGML_ASSERT(head_w && "GLM_DSA MTP: missing LM head (nextn.shared_head_head or model.output)");
|
||||
cur = build_lora_mm(head_w, cur, head_s);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
res->t_logits = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -58,6 +58,7 @@ void llama_model_laguna::load_arch_hparams(llama_model_loader & ml) {
|
|||
|
||||
switch (hparams.n_layer()) {
|
||||
case 40: type = LLM_TYPE_30B_A3B; break; // Laguna-XS.2
|
||||
case 48: type = LLM_TYPE_118B_A8B; break; // Laguna-S.2
|
||||
case 70: type = LLM_TYPE_230B_A10B; break; // Laguna-M.1
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -424,6 +424,22 @@ struct llama_model_mellum : public llama_model_base {
|
|||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
struct llama_model_nanbeige : public llama_model_base {
|
||||
llama_model_nanbeige(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
void load_arch_tensors(llama_model_loader & ml) override;
|
||||
|
||||
int n_loops = 1;
|
||||
int n_layer_phys = 0;
|
||||
bool skip_loop_final_norm = false;
|
||||
|
||||
struct graph : public llm_graph_context {
|
||||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
struct llama_model_qwen : public llama_model_base {
|
||||
llama_model_qwen(const struct llama_model_params & params) : llama_model_base(params) {}
|
||||
void load_arch_hparams(llama_model_loader & ml) override;
|
||||
|
|
@ -1221,6 +1237,10 @@ struct llama_model_glm_dsa : public llama_model_base {
|
|||
graph(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct graph_mtp : public llm_graph_context {
|
||||
graph_mtp(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
|
||||
};
|
||||
|
||||
|
|
|
|||
184
src/models/nanbeige.cpp
Normal file
184
src/models/nanbeige.cpp
Normal file
|
|
@ -0,0 +1,184 @@
|
|||
#include "models.h"
|
||||
|
||||
void llama_model_nanbeige::load_arch_hparams(llama_model_loader & ml) {
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
|
||||
uint32_t n_loops_u = 1;
|
||||
ml.get_key(LLM_KV_NUM_LOOPS, n_loops_u, false);
|
||||
GGML_ASSERT(n_loops_u >= 1);
|
||||
|
||||
skip_loop_final_norm = false;
|
||||
ml.get_key(LLM_KV_SKIP_LOOP_FINAL_NORM, skip_loop_final_norm, false);
|
||||
|
||||
n_layer_phys = (int) hparams.n_layer();
|
||||
|
||||
// Bound-check before casting: signed int mul can overflow and bypass the guard.
|
||||
GGML_ASSERT((size_t) n_layer_phys * (size_t) n_loops_u <= (size_t) LLAMA_MAX_LAYERS);
|
||||
n_loops = (int) n_loops_u;
|
||||
|
||||
// Expand logical layer count before load_tensors() allocates layers / KV.
|
||||
if (n_loops > 1) {
|
||||
for (int j = 1; j < n_loops; ++j) {
|
||||
for (int i = 0; i < n_layer_phys; ++i) {
|
||||
const int dst = i + j * n_layer_phys;
|
||||
hparams.n_head_arr[dst] = hparams.n_head_arr[i];
|
||||
hparams.n_head_kv_arr[dst] = hparams.n_head_kv_arr[i];
|
||||
hparams.n_ff_arr[dst] = hparams.n_ff_arr[i];
|
||||
hparams.is_swa_impl[dst] = hparams.is_swa_impl[i];
|
||||
hparams.is_recr_impl[dst] = hparams.is_recr_impl[i];
|
||||
}
|
||||
}
|
||||
hparams.n_layer_all = (uint32_t) ((size_t) n_layer_phys * (size_t) n_loops);
|
||||
}
|
||||
|
||||
type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
|
||||
void llama_model_nanbeige::load_arch_tensors(llama_model_loader &) {
|
||||
LLAMA_LOAD_LOCALS;
|
||||
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
const int n_phys = n_layer_phys > 0 ? n_layer_phys : n_layer;
|
||||
for (int i = 0; i < n_phys; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
||||
|
||||
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},
|
||||
TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
}
|
||||
|
||||
// Share physical weights across loops; each slot still has its own KV index.
|
||||
if (n_loops > 1) {
|
||||
for (int j = 1; j < n_loops; ++j) {
|
||||
for (int i = 0; i < n_phys; ++i) {
|
||||
layers[i + j * n_phys] = layers[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::unique_ptr<llm_graph_context> llama_model_nanbeige::build_arch_graph(const llm_graph_params & params) const {
|
||||
return std::make_unique<graph>(*this, params);
|
||||
}
|
||||
|
||||
llama_model_nanbeige::graph::graph(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const auto & nb = static_cast<const llama_model_nanbeige &>(model);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
|
||||
|
||||
const int n_phys = nb.n_layer_phys > 0 ? nb.n_layer_phys : (int) n_layer;
|
||||
const int n_loops = nb.n_loops > 0 ? nb.n_loops : 1;
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn = build_attn_inp_kv();
|
||||
|
||||
const float kq_scale = hparams.f_attention_scale == 0.0f
|
||||
? 1.0f / sqrtf(float(n_embd_head))
|
||||
: hparams.f_attention_scale;
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
{
|
||||
ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
|
||||
|
||||
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
|
||||
n_embd_head, n_head, n_head_kv, il);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, rope_factors,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
|
||||
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
|
||||
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
inpL = cur;
|
||||
|
||||
if (n_loops > 1 &&
|
||||
((il + 1) % n_phys) == 0 &&
|
||||
(il + 1) < n_layer &&
|
||||
!nb.skip_loop_final_norm) {
|
||||
cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(cur, "loop_norm", il);
|
||||
inpL = cur;
|
||||
}
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
cur = build_lora_mm(model.output, cur, model.output_s);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
|
@ -116,7 +116,7 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
|
|||
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
if (il == n_layer - 1) {
|
||||
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
|
||||
// skip computing output for unused tokens
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
|
|
@ -154,6 +154,12 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
|
|||
}
|
||||
cur = inpL;
|
||||
|
||||
res->t_h_nextn = cur;
|
||||
|
||||
if (!cparams.embeddings_nextn_masked && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
}
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
|
|
|||
|
|
@ -13,6 +13,14 @@
|
|||
|
||||
struct build_vit_opts {
|
||||
ggml_tensor * attn_mask = nullptr;
|
||||
// TODO @ngxson : merge attn_mask and attn_mask_layers into one call
|
||||
std::vector<ggml_tensor *> attn_mask_layers; // one per layer
|
||||
|
||||
// hook at layer output embeddings
|
||||
std::function<void(ggml_tensor * cur, int il)> callback_layer_out = nullptr;
|
||||
|
||||
// whether to skip the automatic post-layernorm (model.post_ln_w) applied at the end
|
||||
bool skip_post_ln = false;
|
||||
};
|
||||
|
||||
struct clip_graph {
|
||||
|
|
|
|||
|
|
@ -82,6 +82,13 @@
|
|||
#define KEY_A_PROJ_WINDOW_SIZE "clip.audio.projector.window_size"
|
||||
#define KEY_A_PROJ_DOWNSAMPLE_RATE "clip.audio.projector.downsample_rate"
|
||||
#define KEY_A_PROJ_HEAD_COUNT "clip.audio.projector.head_count"
|
||||
#define KEY_A_RVQ_NUM_QUANTIZERS "clip.audio.rvq.num_quantizers" // mimo-audio-tokenizer
|
||||
#define KEY_A_RVQ_CODEBOOK_SIZE "clip.audio.rvq.codebook_size" // mimo-audio-tokenizer: per-quantizer bin count
|
||||
#define KEY_A_WA_PATTERN_MODE "clip.audio.wa_pattern_mode" // mimo-audio-tokenizer, per-layer -1 (full) / 0 (windowed)
|
||||
#define KEY_A_ATTN_WINDOW_SIZE "clip.audio.window_size" // mimo-audio-tokenizer: sliding-window radius
|
||||
#define KEY_A_LOCAL_BLOCK_COUNT "clip.audio.local_block_count" // mimo-v2.5: input_local_transformer layer count
|
||||
#define KEY_A_LOCAL_GROUP_SIZE "clip.audio.local_group_size" // mimo-v2.5: input_local_transformer grouping size
|
||||
#define KEY_AUDIO_SUBSAMPLING_FACTOR "clip.audio.subsampling_factor"
|
||||
|
||||
//
|
||||
// tensor name constants
|
||||
|
|
@ -175,6 +182,24 @@
|
|||
#define TN_MM_NORM_PRE "mm.a.norm_pre.%s"
|
||||
#define TN_MM_NORM_MID "mm.a.norm_mid.%s"
|
||||
|
||||
// mimo-audio-tokenizer
|
||||
#define TN_A_DOWNSAMPLE_CONV "a.downsample.conv.%s"
|
||||
#define TN_A_DOWNSAMPLE_NORM "a.downsample.norm.%s"
|
||||
#define TN_A_RVQ_CODEBOOK "a.rvq.codebook.%s"
|
||||
// mimo-v2.5: text-side RVQ code embedding ("text codebook")
|
||||
#define TN_MM_A_CODE_EMBD "mm.a.code_embd.%s"
|
||||
// mimo-v2.5: LLM-side connector (input_local_transformer)
|
||||
#define TN_MM_A_LOCAL_ATTN_Q "mm.a.local_blk.%d.attn_q.%s"
|
||||
#define TN_MM_A_LOCAL_ATTN_K "mm.a.local_blk.%d.attn_k.%s"
|
||||
#define TN_MM_A_LOCAL_ATTN_V "mm.a.local_blk.%d.attn_v.%s"
|
||||
#define TN_MM_A_LOCAL_ATTN_OUT "mm.a.local_blk.%d.attn_out.%s"
|
||||
#define TN_MM_A_LOCAL_FFN_GATE "mm.a.local_blk.%d.ffn_gate.%s"
|
||||
#define TN_MM_A_LOCAL_FFN_UP "mm.a.local_blk.%d.ffn_up.%s"
|
||||
#define TN_MM_A_LOCAL_FFN_DOWN "mm.a.local_blk.%d.ffn_down.%s"
|
||||
#define TN_MM_A_LOCAL_LN1 "mm.a.local_blk.%d.ln1.%s"
|
||||
#define TN_MM_A_LOCAL_LN2 "mm.a.local_blk.%d.ln2.%s"
|
||||
#define TN_MM_A_LOCAL_NORM "mm.a.local_norm.%s"
|
||||
|
||||
// cogvlm
|
||||
#define TN_MM_POST_FC_NORM "mm.post_fc_norm.%s"
|
||||
#define TN_MM_H_TO_4H "mm.up.%s"
|
||||
|
|
@ -314,6 +339,12 @@
|
|||
#define TN_YASA_STAGE_DOWN_CONV "v.stage.%d.down.conv.%s"
|
||||
#define TN_YASA_STAGE_BLK "v.stage.%d.blk.%d.%s.%s"
|
||||
|
||||
// parakeet
|
||||
#define TN_MEL_FILTERS "a.mel_filters"
|
||||
#define TN_WINDOW "a.window"
|
||||
#define TN_CONV_NORM_MEAN "%s.blk.%d.conv_norm_mean"
|
||||
#define TN_CONV_NORM_VAR "%s.blk.%d.conv_norm_var"
|
||||
|
||||
// align x to upper multiple of n
|
||||
#define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
|
||||
|
||||
|
|
@ -368,12 +399,14 @@ enum projector_type {
|
|||
PROJECTOR_TYPE_KIMIK25,
|
||||
PROJECTOR_TYPE_NEMOTRON_V2_VL,
|
||||
PROJECTOR_TYPE_HUNYUANVL,
|
||||
PROJECTOR_TYPE_PARAKEET,
|
||||
PROJECTOR_TYPE_EXAONE4_5,
|
||||
PROJECTOR_TYPE_MINICPMV4_6,
|
||||
PROJECTOR_TYPE_GRANITE_SPEECH,
|
||||
PROJECTOR_TYPE_MIMOVL,
|
||||
PROJECTOR_TYPE_MINIMAX_M3,
|
||||
PROJECTOR_TYPE_GRANITE4_VISION,
|
||||
PROJECTOR_TYPE_MIMO_AUDIO,
|
||||
PROJECTOR_TYPE_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -429,6 +462,8 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
{ PROJECTOR_TYPE_MIMOVL, "mimovl"},
|
||||
{ PROJECTOR_TYPE_MINIMAX_M3, "minimax_m3"},
|
||||
{ PROJECTOR_TYPE_GRANITE4_VISION, "granite4_vision"},
|
||||
{ PROJECTOR_TYPE_MIMO_AUDIO, "mimo_audio"},
|
||||
{ PROJECTOR_TYPE_PARAKEET, "parakeet"},
|
||||
};
|
||||
|
||||
static projector_type clip_projector_type_from_string(const std::string & str) {
|
||||
|
|
|
|||
|
|
@ -110,6 +110,8 @@ struct clip_hparams {
|
|||
// audio
|
||||
int32_t n_mel_bins = 0; // whisper preprocessor
|
||||
int32_t proj_stack_factor = 0; // ultravox
|
||||
int32_t subsampling_factor = 0; // parakeet
|
||||
|
||||
int32_t audio_chunk_size = 0;
|
||||
int32_t audio_conv_kernel_size = 0;
|
||||
int32_t audio_max_pos_emb = 0;
|
||||
|
|
@ -124,6 +126,18 @@ struct clip_hparams {
|
|||
int32_t audio_window_len = -1;
|
||||
int32_t audio_hop_len = -1;
|
||||
|
||||
// parakeet
|
||||
std::vector<float> mel_filters;
|
||||
std::vector<float> window;
|
||||
|
||||
// mimo-audio-tokenizer: residual vector quantizer
|
||||
int32_t rvq_num_quantizers = 0;
|
||||
std::vector<int32_t> rvq_codebook_size; // per-quantizer bin count (ragged, e.g. 1024/1024/256/128x17)
|
||||
|
||||
// mimo-v2.5: LLM-side connector (input_local_transformer)
|
||||
int32_t audio_local_n_layer = 0;
|
||||
int32_t audio_local_group_size = 0;
|
||||
|
||||
// legacy
|
||||
bool has_llava_projector = false;
|
||||
int minicpmv_version = 0;
|
||||
|
|
@ -237,14 +251,16 @@ struct clip_layer {
|
|||
ggml_tensor * norm_conv_b = nullptr;
|
||||
ggml_tensor * linear_pos_w = nullptr;
|
||||
|
||||
ggml_tensor * conv_norm_w = nullptr;
|
||||
ggml_tensor * conv_norm_b = nullptr;
|
||||
ggml_tensor * conv_dw_w = nullptr;
|
||||
ggml_tensor * conv_dw_b = nullptr;
|
||||
ggml_tensor * conv_pw1_w = nullptr;
|
||||
ggml_tensor * conv_pw1_b = nullptr;
|
||||
ggml_tensor * conv_pw2_w = nullptr;
|
||||
ggml_tensor * conv_pw2_b = nullptr;
|
||||
ggml_tensor * conv_norm_w = nullptr;
|
||||
ggml_tensor * conv_norm_b = nullptr;
|
||||
ggml_tensor * conv_norm_mean = nullptr; // parakeet
|
||||
ggml_tensor * conv_norm_var = nullptr; // parakeet
|
||||
ggml_tensor * conv_dw_w = nullptr;
|
||||
ggml_tensor * conv_dw_b = nullptr;
|
||||
ggml_tensor * conv_pw1_w = nullptr;
|
||||
ggml_tensor * conv_pw1_b = nullptr;
|
||||
ggml_tensor * conv_pw2_w = nullptr;
|
||||
ggml_tensor * conv_pw2_b = nullptr;
|
||||
|
||||
// gemma4 audio conformer per-layer
|
||||
ggml_tensor * attn_pre_norm_w = nullptr;
|
||||
|
|
@ -537,6 +553,20 @@ struct clip_model {
|
|||
ggml_tensor * mm_norm_pre_b = nullptr;
|
||||
ggml_tensor * mm_norm_mid_w = nullptr;
|
||||
|
||||
// mimo-audio-tokenizer: post-transformer downsample + RVQ codebook
|
||||
ggml_tensor * downsample_conv_w = nullptr; // no bias
|
||||
ggml_tensor * downsample_norm_w = nullptr;
|
||||
ggml_tensor * downsample_norm_b = nullptr;
|
||||
ggml_tensor * rvq_codebook = nullptr; // merged 3D [n_q, max_bins, dim]
|
||||
|
||||
// mimo-v2.5: text-side RVQ code embedding ("text codebook")
|
||||
ggml_tensor * mm_a_code_embd = nullptr; // merged 3D [n_channels, vocab, dim]
|
||||
|
||||
// mimo-v2.5: LLM-side connector (input_local_transformer, separate from the
|
||||
// audio_tokenizer's own encoder `layers`)
|
||||
std::vector<clip_layer> mm_a_local_layers;
|
||||
ggml_tensor * mm_a_local_norm_w = nullptr;
|
||||
|
||||
// qwen3a
|
||||
ggml_tensor * conv2d_1_w = nullptr;
|
||||
ggml_tensor * conv2d_1_b = nullptr;
|
||||
|
|
|
|||
|
|
@ -61,11 +61,13 @@
|
|||
#include "models/minicpmv.cpp"
|
||||
#include "models/minimax-m3.cpp"
|
||||
#include "models/paddleocr.cpp"
|
||||
#include "models/parakeet.cpp"
|
||||
#include "models/pixtral.cpp"
|
||||
#include "models/qwen2vl.cpp"
|
||||
#include "models/qwen3vl.cpp"
|
||||
#include "models/mimovl.cpp"
|
||||
#include "models/qwen3a.cpp"
|
||||
#include "models/mimovl.cpp"
|
||||
#include "models/mimo-audio.cpp"
|
||||
#include "models/step3vl.cpp"
|
||||
#include "models/siglip.cpp"
|
||||
#include "models/whisper-enc.cpp"
|
||||
|
|
@ -389,6 +391,11 @@ ggml_tensor * clip_graph::build_vit(
|
|||
auto & layer = model.layers[il];
|
||||
ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
|
||||
|
||||
ggml_tensor * attn_mask = opts.attn_mask;
|
||||
if (opts.attn_mask_layers.size() > (size_t) il) {
|
||||
attn_mask = opts.attn_mask_layers[il];
|
||||
}
|
||||
|
||||
// layernorm1
|
||||
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
|
||||
cb(cur, "layer_inp_normed", il);
|
||||
|
|
@ -501,7 +508,7 @@ ggml_tensor * clip_graph::build_vit(
|
|||
|
||||
// build_attn returns a flat 2D [n_embd, n_pos*B]
|
||||
cur = build_attn(layer.o_w, layer.o_b,
|
||||
Qcur, Kcur, Vcur, opts.attn_mask, kq_scale, il);
|
||||
Qcur, Kcur, Vcur, attn_mask, kq_scale, il);
|
||||
cb(cur, "attn_out", il);
|
||||
}
|
||||
|
||||
|
|
@ -520,6 +527,10 @@ ggml_tensor * clip_graph::build_vit(
|
|||
|
||||
inpL = cur; // inpL = residual, cur = hidden_states
|
||||
|
||||
if (opts.callback_layer_out) {
|
||||
opts.callback_layer_out(cur, il);
|
||||
}
|
||||
|
||||
cb(cur, "ffn_inp", il);
|
||||
|
||||
// layernorm2 (pre-ffn norm)
|
||||
|
|
@ -568,7 +579,7 @@ ggml_tensor * clip_graph::build_vit(
|
|||
}
|
||||
|
||||
// post-layernorm
|
||||
if (model.post_ln_w) {
|
||||
if (model.post_ln_w && !opts.skip_post_ln) {
|
||||
inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1);
|
||||
}
|
||||
|
||||
|
|
@ -1061,6 +1072,10 @@ static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const
|
|||
{
|
||||
builder = std::make_unique<clip_graph_qwen3a>(ctx, img);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_mimo_audio>(ctx, img);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_youtuvl>(ctx, img);
|
||||
|
|
@ -1069,6 +1084,10 @@ static std::unique_ptr<clip_graph> clip_get_graph_builder(clip_ctx * ctx, const
|
|||
{
|
||||
builder = std::make_unique<clip_graph_yasa2>(ctx, img);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_parakeet>(ctx, img);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GRANITE4_VISION:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_granite4_vision>(ctx, img);
|
||||
|
|
@ -1415,6 +1434,20 @@ struct clip_model_loader {
|
|||
{
|
||||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
get_u32(KEY_AUDIO_SUBSAMPLING_FACTOR, hparams.subsampling_factor);
|
||||
GGML_ASSERT(hparams.subsampling_factor == 8 &&
|
||||
"subsampling_factor must match the conv strides in clip_graph_parakeet::build()");
|
||||
get_u32(KEY_A_CONV_KERNEL_SIZE, hparams.audio_conv_kernel_size);
|
||||
GGML_ASSERT(hparams.audio_conv_kernel_size > 0 && hparams.audio_conv_kernel_size % 2 == 1 &&
|
||||
"audio_conv_kernel_size must be a positive odd integer");
|
||||
hparams.audio_chunk_len = 0;
|
||||
hparams.audio_sample_rate = 16000;
|
||||
hparams.audio_n_fft = 512;
|
||||
hparams.audio_window_len = 400;
|
||||
hparams.audio_hop_len = 160;
|
||||
} break;
|
||||
case PROJECTOR_TYPE_IDEFICS3:
|
||||
{
|
||||
// use default llava-uhd preprocessing params
|
||||
|
|
@ -1652,6 +1685,45 @@ struct clip_model_loader {
|
|||
hparams.audio_window_len = 400;
|
||||
hparams.audio_hop_len = 160;
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
get_u32(KEY_A_RVQ_NUM_QUANTIZERS, hparams.rvq_num_quantizers, false);
|
||||
get_arr_int(KEY_A_RVQ_CODEBOOK_SIZE, hparams.rvq_codebook_size, false);
|
||||
if (hparams.rvq_num_quantizers <= 0) {
|
||||
throw std::runtime_error(string_format("%s: mimo_audio: missing %s\n", __func__, KEY_A_RVQ_NUM_QUANTIZERS));
|
||||
}
|
||||
if ((int) hparams.rvq_codebook_size.size() != hparams.rvq_num_quantizers) {
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: mimo_audio: %s length (%zu) must equal %s (%d)\n", __func__,
|
||||
KEY_A_RVQ_CODEBOOK_SIZE, hparams.rvq_codebook_size.size(),
|
||||
KEY_A_RVQ_NUM_QUANTIZERS, hparams.rvq_num_quantizers));
|
||||
}
|
||||
hparams.ffn_op = FFN_GELU_ERF; // PyTorch F.gelu default (approximate="none")
|
||||
hparams.rope_theta = 10000.0f;
|
||||
|
||||
// audio preprocessing params (mel spectrogram)
|
||||
hparams.audio_sample_rate = 24000;
|
||||
hparams.audio_n_fft = 960;
|
||||
hparams.audio_window_len = 960;
|
||||
hparams.audio_hop_len = 240;
|
||||
|
||||
get_u32(KEY_A_ATTN_WINDOW_SIZE, hparams.attn_window_size);
|
||||
std::vector<int> wa_pattern;
|
||||
get_arr_int(KEY_A_WA_PATTERN_MODE, wa_pattern, true);
|
||||
if ((int) wa_pattern.size() != hparams.n_layer) {
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: mimo_audio: %s length (%zu) must equal n_layer (%d)\n", __func__,
|
||||
KEY_A_WA_PATTERN_MODE, wa_pattern.size(), hparams.n_layer));
|
||||
}
|
||||
hparams.wa_pattern_mode.assign(wa_pattern.begin(), wa_pattern.end());
|
||||
|
||||
get_u32(KEY_A_LOCAL_BLOCK_COUNT, hparams.audio_local_n_layer);
|
||||
get_u32(KEY_A_LOCAL_GROUP_SIZE, hparams.audio_local_group_size);
|
||||
if (hparams.audio_local_group_size <= 0) {
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: mimo_audio: %s must be > 0\n", __func__, KEY_A_LOCAL_GROUP_SIZE));
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PADDLEOCR:
|
||||
{
|
||||
hparams.n_merge = 2;
|
||||
|
|
@ -1923,16 +1995,46 @@ struct clip_model_loader {
|
|||
return cur;
|
||||
};
|
||||
|
||||
auto get_scalar = [&](const std::string & name, float default_val) {
|
||||
auto get_vector = [&](const std::string & name) {
|
||||
std::vector<float> result;
|
||||
auto it = tensor_offset.find(name);
|
||||
if (it == tensor_offset.end()) {
|
||||
return result;
|
||||
}
|
||||
|
||||
const int64_t idx = gguf_find_tensor(ctx_gguf.get(), name.c_str());
|
||||
if (idx < 0) {
|
||||
throw std::runtime_error(string_format("%s: failed to find tensor %s\n", __func__, name.c_str()));
|
||||
}
|
||||
|
||||
if (const auto type = gguf_get_tensor_type(ctx_gguf.get(), idx); type != GGML_TYPE_F32) {
|
||||
throw std::runtime_error(string_format("%s: %s must be %s, was %s\n", __func__,
|
||||
name.c_str(), ggml_type_name(GGML_TYPE_F32), ggml_type_name(type)));
|
||||
}
|
||||
|
||||
const size_t n_bytes = gguf_get_tensor_size(ctx_gguf.get(), idx);
|
||||
if (n_bytes == 0) {
|
||||
throw std::runtime_error(string_format("%s: tensor %s is empty\n", __func__, name.c_str()));
|
||||
}
|
||||
|
||||
const size_t n_elems = n_bytes / sizeof(float);
|
||||
result.resize(n_elems);
|
||||
fin.seekg(it->second, std::ios::beg);
|
||||
fin.read(reinterpret_cast<char*>(result.data()), n_bytes);
|
||||
return result;
|
||||
};
|
||||
|
||||
auto get_scalar = [&](const std::string & name, float default_val) {
|
||||
auto v = get_vector(name);
|
||||
if (v.empty()) {
|
||||
return default_val;
|
||||
}
|
||||
size_t offset = it->second;
|
||||
fin.seekg(offset, std::ios::beg);
|
||||
float value;
|
||||
fin.read(reinterpret_cast<char*>(&value), sizeof(float));
|
||||
return value;
|
||||
if (v.size() != 1) {
|
||||
throw std::runtime_error(string_format("%s: expected scalar tensor '%s' but got %d elements\n",
|
||||
__func__, name.c_str(), (int) v.size()));
|
||||
}
|
||||
|
||||
return v[0];
|
||||
};
|
||||
|
||||
model.class_embedding = get_tensor(TN_CLASS_EMBD, false);
|
||||
|
|
@ -2526,6 +2628,54 @@ struct clip_model_loader {
|
|||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||||
model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||||
model.downsample_conv_w = get_tensor(string_format(TN_A_DOWNSAMPLE_CONV, "weight"));
|
||||
model.downsample_norm_w = get_tensor(string_format(TN_A_DOWNSAMPLE_NORM, "weight"));
|
||||
model.downsample_norm_b = get_tensor(string_format(TN_A_DOWNSAMPLE_NORM, "bias"));
|
||||
model.rvq_codebook = get_tensor(string_format(TN_A_RVQ_CODEBOOK, "weight"), false);
|
||||
model.mm_a_code_embd = get_tensor(string_format(TN_MM_A_CODE_EMBD, "weight"), false);
|
||||
if (!model.rvq_codebook || !model.mm_a_code_embd) {
|
||||
throw std::runtime_error(string_format("%s: mimo_audio: missing %s or %s\n", __func__,
|
||||
TN_A_RVQ_CODEBOOK, TN_MM_A_CODE_EMBD));
|
||||
}
|
||||
// hparams.rvq_codebook_size comes from GGUF metadata and is independent of the
|
||||
// tensors' actual shapes - bound it so codebook/code_embd views built from it
|
||||
// (mimo-audio.cpp) can never read past either tensor's allocated bins/vocab.
|
||||
for (int32_t bins : hparams.rvq_codebook_size) {
|
||||
if (bins <= 0 || bins > model.rvq_codebook->ne[1] || bins > model.mm_a_code_embd->ne[1]) {
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: mimo_audio: %s entry (%d) out of range for codebook/code_embd tensors\n",
|
||||
__func__, KEY_A_RVQ_CODEBOOK_SIZE, bins));
|
||||
}
|
||||
}
|
||||
|
||||
// LLM-side connector: input_local_transformer + projection
|
||||
model.mm_a_local_layers.resize(hparams.audio_local_n_layer);
|
||||
for (int il = 0; il < hparams.audio_local_n_layer; il++) {
|
||||
auto & layer = model.mm_a_local_layers[il];
|
||||
layer.q_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_Q, il, "weight"));
|
||||
layer.q_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_Q, il, "bias"));
|
||||
layer.k_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_K, il, "weight"));
|
||||
layer.k_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_K, il, "bias"));
|
||||
layer.v_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_V, il, "weight"));
|
||||
layer.v_b = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_V, il, "bias"));
|
||||
layer.o_w = get_tensor(string_format(TN_MM_A_LOCAL_ATTN_OUT, il, "weight"));
|
||||
layer.ff_gate_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_GATE, il, "weight"));
|
||||
layer.ff_up_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_UP, il, "weight"));
|
||||
layer.ff_down_w = get_tensor(string_format(TN_MM_A_LOCAL_FFN_DOWN, il, "weight"));
|
||||
layer.ln_1_w = get_tensor(string_format(TN_MM_A_LOCAL_LN1, il, "weight"));
|
||||
layer.ln_2_w = get_tensor(string_format(TN_MM_A_LOCAL_LN2, il, "weight"));
|
||||
}
|
||||
model.mm_a_local_norm_w = get_tensor(string_format(TN_MM_A_LOCAL_NORM, "weight"));
|
||||
|
||||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
{
|
||||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||||
|
|
@ -2782,6 +2932,68 @@ struct clip_model_loader {
|
|||
layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias"));
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
|
||||
hparams.mel_filters = get_vector(TN_MEL_FILTERS);
|
||||
hparams.window = get_vector(TN_WINDOW);
|
||||
|
||||
// Subsampling layers (conv1d)
|
||||
for (int i : {0, 2, 3, 5, 6}) {
|
||||
model.pre_encode_conv_X_w[i] = get_tensor(string_format(TN_CONV1D, i, "weight"));
|
||||
model.pre_encode_conv_X_b[i] = get_tensor(string_format(TN_CONV1D, i, "bias"));
|
||||
}
|
||||
model.pre_encode_out_w = get_tensor(string_format(TN_PRE_ENCODE_OUT, "weight"));
|
||||
model.pre_encode_out_b = get_tensor(string_format(TN_PRE_ENCODE_OUT, "bias"));
|
||||
|
||||
// Projection layers
|
||||
model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"), false);
|
||||
model.mm_0_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"), false);
|
||||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"), false);
|
||||
|
||||
// Encoder layers
|
||||
for (int il = 0; il < hparams.n_layer; ++il) {
|
||||
auto & layer = model.layers[il];
|
||||
|
||||
// Attention (from shared above)
|
||||
|
||||
// Relative position encoding
|
||||
layer.linear_pos_w = get_tensor(string_format(TN_LINEAR_POS, prefix, il, "weight"));
|
||||
layer.pos_bias_u = get_tensor(string_format(TN_POS_BIAS_U, prefix, il));
|
||||
layer.pos_bias_v = get_tensor(string_format(TN_POS_BIAS_V, prefix, il));
|
||||
|
||||
// Convolution module
|
||||
layer.conv_pw1_w = get_tensor(string_format(TN_CONV_PW1, prefix, il, "weight"));
|
||||
layer.conv_pw1_b = get_tensor(string_format(TN_CONV_PW1, prefix, il, "bias"), false);
|
||||
layer.conv_dw_w = get_tensor(string_format(TN_CONV_DW, prefix, il, "weight"));
|
||||
layer.conv_dw_b = get_tensor(string_format(TN_CONV_DW, prefix, il, "bias"), false);
|
||||
layer.conv_norm_w = get_tensor(string_format(TN_CONV_NORM, prefix, il, "weight"));
|
||||
layer.conv_norm_b = get_tensor(string_format(TN_CONV_NORM, prefix, il, "bias"));
|
||||
layer.conv_norm_mean = get_tensor(string_format(TN_CONV_NORM_MEAN, prefix, il));
|
||||
layer.conv_norm_var = get_tensor(string_format(TN_CONV_NORM_VAR, prefix, il));
|
||||
layer.conv_pw2_w = get_tensor(string_format(TN_CONV_PW2, prefix, il, "weight"));
|
||||
layer.conv_pw2_b = get_tensor(string_format(TN_CONV_PW2, prefix, il, "bias"), false);
|
||||
|
||||
// Feed-forward networks
|
||||
layer.ff_norm_w = get_tensor(string_format(TN_FFN_NORM, prefix, il, "weight"));
|
||||
layer.ff_norm_b = get_tensor(string_format(TN_FFN_NORM, prefix, il, "bias"));
|
||||
|
||||
layer.ff_norm_1_w = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "weight"));
|
||||
layer.ff_norm_1_b = get_tensor(string_format(TN_FFN_NORM_1, prefix, il, "bias"));
|
||||
layer.ff_up_1_w = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "weight"));
|
||||
layer.ff_up_1_b = get_tensor(string_format(TN_FFN_UP_1, prefix, il, "bias"), false);
|
||||
layer.ff_down_1_w = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "weight"));
|
||||
layer.ff_down_1_b = get_tensor(string_format(TN_FFN_DOWN_1, prefix, il, "bias"), false);
|
||||
|
||||
// Layer norms
|
||||
layer.norm_conv_w = get_tensor(string_format(TN_NORM_CONV, prefix, il, "weight"));
|
||||
layer.norm_conv_b = get_tensor(string_format(TN_NORM_CONV, prefix, il, "bias"));
|
||||
}
|
||||
|
||||
model.mm_model_mlp_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
||||
model.mm_model_mlp_2_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
||||
model.mm_model_mlp_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GRANITE_SPEECH:
|
||||
{
|
||||
model.inp_proj_w = get_tensor(string_format(TN_INP_PROJ, "weight"));
|
||||
|
|
@ -3627,10 +3839,23 @@ int clip_n_output_tokens(const clip_ctx * ctx, const clip_image_f32 * img) {
|
|||
}
|
||||
n_patches = n;
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
n_patches = (img->nx() + (params.subsampling_factor - 1)) / params.subsampling_factor;
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GEMMA4UA:
|
||||
{
|
||||
n_patches = img->nx(); // no downsampling: one token per raw waveform frame
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
// conv1(s=1) + conv2(s=2) -> RVQ-encoder downsample conv(k=2,s=2)
|
||||
int n = img->nx();
|
||||
n = (n - 1) / 2 + 1; // conv1 + conv2
|
||||
n = (n - 2) / 2 + 1; // downsample conv
|
||||
const int group_size = params.audio_local_group_size;
|
||||
n_patches = (n + group_size - 1) / group_size;
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GRANITE_SPEECH:
|
||||
{
|
||||
const int ws = ctx->model.hparams.audio_proj_window_size;
|
||||
|
|
@ -4458,6 +4683,58 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
|
|||
set_input_f32("pos_emb", pos_emb);
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
GGML_ASSERT(imgs.entries.size() == 1);
|
||||
const int n_frames = imgs.entries.front().nx();
|
||||
const int n_pos = (n_frames - 1) / 2 + 1; // matches conv1(s=1)+conv2(s=2) output length
|
||||
|
||||
std::vector<int32_t> positions(n_pos);
|
||||
for (int i = 0; i < n_pos; i++) {
|
||||
positions[i] = i;
|
||||
}
|
||||
set_input_i32("mimo_audio_positions", positions);
|
||||
|
||||
const int window = hparams.attn_window_size;
|
||||
GGML_ASSERT(window > 0);
|
||||
|
||||
const float neg_inf = std::numeric_limits<float>::lowest();
|
||||
std::vector<float> full_mask((size_t) n_pos * n_pos);
|
||||
std::vector<float> window_mask((size_t) n_pos * n_pos);
|
||||
for (int q = 0; q < n_pos; q++) {
|
||||
for (int k = 0; k < n_pos; k++) {
|
||||
const bool causal_ok = k <= q;
|
||||
full_mask[(size_t) q * n_pos + k] = causal_ok ? 0.0f : neg_inf;
|
||||
window_mask[(size_t) q * n_pos + k] = (causal_ok && (q - k) <= window) ? 0.0f : neg_inf;
|
||||
}
|
||||
}
|
||||
set_input_f32("mimo_audio_full_mask", full_mask);
|
||||
set_input_f32("mimo_audio_window_mask", window_mask);
|
||||
|
||||
// input_local_transformer: block-diagonal mask + in-group positions
|
||||
{
|
||||
const int n_pos_ds = (n_pos - 2) / 2 + 1; // matches downsample conv (k=2,s=2,p=0)
|
||||
const int group_size = hparams.audio_local_group_size;
|
||||
GGML_ASSERT(group_size > 0);
|
||||
const int n_groups = (n_pos_ds + group_size - 1) / group_size;
|
||||
const int n_padded = n_groups * group_size;
|
||||
|
||||
std::vector<int32_t> local_positions(n_padded);
|
||||
for (int i = 0; i < n_padded; i++) {
|
||||
local_positions[i] = i % group_size;
|
||||
}
|
||||
set_input_i32("mimo_audio_local_positions", local_positions);
|
||||
|
||||
std::vector<float> local_mask((size_t) n_padded * n_padded);
|
||||
for (int q = 0; q < n_padded; q++) {
|
||||
for (int k = 0; k < n_padded; k++) {
|
||||
const bool same_group = (q / group_size) == (k / group_size);
|
||||
local_mask[(size_t) q * n_padded + k] = same_group ? 0.0f : neg_inf;
|
||||
}
|
||||
}
|
||||
set_input_f32("mimo_audio_local_mask", local_mask);
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_LFM2A:
|
||||
{
|
||||
GGML_ASSERT(imgs.entries.size() == 1);
|
||||
|
|
@ -4479,6 +4756,88 @@ bool clip_image_batch_encode(clip_ctx * ctx, int n_threads, const clip_image_f32
|
|||
}
|
||||
set_input_f32("pos_emb", pos_emb);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
GGML_ASSERT(imgs.entries.size() == 1);
|
||||
struct ggml_tensor * attn_mask = ggml_graph_get_tensor(gf, "attn_mask");
|
||||
const int n_q = attn_mask->ne[1];
|
||||
const int n_k = attn_mask->ne[0];
|
||||
const int n_frames = imgs.entries.front().nx();
|
||||
const int n_tokens_real = (n_frames + hparams.subsampling_factor-1) / hparams.subsampling_factor;
|
||||
const float mask_value = -1e30f;
|
||||
|
||||
std::vector<float> mask_data(n_q * n_k);
|
||||
if (n_k == n_q) {
|
||||
// full attention: mask keys that are padding
|
||||
for (int q = 0; q < n_q; ++q) {
|
||||
for (int k = 0; k < n_k; ++k) {
|
||||
mask_data[q * n_k + k] = (k >= n_tokens_real) ? mask_value : 0.0f;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// local attention: mask keys outside the valid window
|
||||
const int att_left = n_k / 2;
|
||||
for (int q = 0; q < n_q; ++q) {
|
||||
for (int k = 0; k < n_k; ++k) {
|
||||
const int key = q - att_left + k;
|
||||
mask_data[q * n_k + k] = (key >= 0 && key < n_tokens_real) ? 0.0f : mask_value;
|
||||
}
|
||||
}
|
||||
}
|
||||
set_input_f32(attn_mask->name, mask_data);
|
||||
|
||||
// local attention skew mask: zeroes out the probs that were
|
||||
// computed for keys outside the valid sliding window.
|
||||
if (struct ggml_tensor * local_mask = ggml_graph_get_tensor(gf, "local_mask")) {
|
||||
const int lm_k = local_mask->ne[0];
|
||||
const int lm_q = local_mask->ne[1];
|
||||
const int window_size = lm_k - lm_q + 1;
|
||||
std::vector<float> lm_data(lm_q * lm_k);
|
||||
for (int q = 0; q < lm_q; ++q) {
|
||||
for (int k = 0; k < lm_k; ++k) {
|
||||
const int rel = k - q;
|
||||
lm_data[q * lm_k + k] = (rel >= 0 && rel < window_size) ? 1.0f : 0.0f;
|
||||
}
|
||||
}
|
||||
set_input_f32(local_mask->name, lm_data);
|
||||
}
|
||||
|
||||
// Generate rotation frequencies for relative positional encoding.
|
||||
{
|
||||
const int n_state = hparams.n_embd;
|
||||
const int d_half = n_state / 2;
|
||||
const float log_10000 = logf(10000.0f);
|
||||
std::vector<float> freqs(d_half);
|
||||
for (int k = 0; k < d_half; ++k) {
|
||||
freqs[k] = expf(-(float(k * 2) * log_10000 / float(n_state)));
|
||||
}
|
||||
set_input_f32("pos_freqs", freqs);
|
||||
}
|
||||
|
||||
// Generate relative positional distance values which scaled by
|
||||
// the frequency to produce the angles for sin/cos.
|
||||
{
|
||||
// window_size is only known after graph construction since it depends on
|
||||
// n_time from the conv output, so we read it back from the graph tensor.
|
||||
struct ggml_tensor * rel_pos = ggml_graph_get_tensor(gf, "rel_positions");
|
||||
const int window_size = rel_pos->ne[1];
|
||||
std::vector<float> pos(window_size);
|
||||
// local attention: window is fixed at [att_left, att_right]
|
||||
// full attention: window covers the full sequence, centered
|
||||
if (ggml_graph_get_tensor(gf, "local_mask")) {
|
||||
const int att_left = window_size / 2;
|
||||
for (int t = 0; t < window_size; ++t) {
|
||||
pos[t] = float(att_left - t);
|
||||
}
|
||||
} else {
|
||||
const int n_time = (window_size + 1) / 2;
|
||||
for (int t = 0; t < window_size; ++t) {
|
||||
pos[t] = float(n_time - 1 - t);
|
||||
}
|
||||
}
|
||||
set_input_f32(rel_pos->name, pos);
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GRANITE_SPEECH:
|
||||
{
|
||||
const int context_size = ctx->model.hparams.audio_chunk_size;
|
||||
|
|
@ -4760,6 +5119,10 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
return ctx->model.qf_proj_blocks.size() * ctx->model.hparams.projection_dim;
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
return ctx->model.mm_ffn_down_w->ne[1];
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
return ctx->model.mm_2_w->ne[1];
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
return ctx->model.mm_1_w->ne[1];
|
||||
default:
|
||||
GGML_ABORT("Unknown projector type");
|
||||
}
|
||||
|
|
|
|||
218
tools/mtmd/models/mimo-audio.cpp
Normal file
218
tools/mtmd/models/mimo-audio.cpp
Normal file
|
|
@ -0,0 +1,218 @@
|
|||
#include "models.h"
|
||||
|
||||
ggml_cgraph * clip_graph_mimo_audio::build() {
|
||||
ggml_tensor * inp = build_inp_raw(1); // [n_frames, n_mel, 1]
|
||||
|
||||
ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, model.conv1d_1_b);
|
||||
cur = ggml_gelu_erf(ctx0, cur);
|
||||
|
||||
cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1);
|
||||
cur = ggml_add(ctx0, cur, model.conv1d_2_b);
|
||||
cur = ggml_gelu_erf(ctx0, cur);
|
||||
|
||||
ggml_tensor * inpL = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); // [n_embd, n_pos]
|
||||
const int64_t n_pos = inpL->ne[1];
|
||||
cb(inpL, "after_conv1d", -1);
|
||||
|
||||
GGML_ASSERT((int) hparams.wa_pattern_mode.size() == n_layer);
|
||||
|
||||
ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
|
||||
ggml_set_name(inp_pos, "mimo_audio_positions");
|
||||
ggml_set_input(inp_pos);
|
||||
|
||||
ggml_tensor * full_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
|
||||
ggml_set_name(full_mask, "mimo_audio_full_mask");
|
||||
ggml_set_input(full_mask);
|
||||
|
||||
ggml_tensor * window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
|
||||
ggml_set_name(window_mask, "mimo_audio_window_mask");
|
||||
ggml_set_input(window_mask);
|
||||
|
||||
build_vit_opts opts;
|
||||
opts.attn_mask_layers.resize(n_layer);
|
||||
for (int il = 0; il < n_layer; il++) {
|
||||
opts.attn_mask_layers[il] = hparams.wa_pattern_mode[il] == -1 ? full_mask : window_mask;
|
||||
}
|
||||
// the skip connection below must be added before the post-transformer norm,
|
||||
// so build_vit must not apply that norm itself
|
||||
opts.skip_post_ln = true;
|
||||
|
||||
// encoder_skip_layer_id=3 (1-indexed) -> capture output of layer index 2
|
||||
const int skip_capture_il = 2;
|
||||
GGML_ASSERT(n_layer > skip_capture_il);
|
||||
ggml_tensor * skip_hidden = nullptr;
|
||||
opts.callback_layer_out = [&](ggml_tensor * layer_cur, int il) {
|
||||
if (il == skip_capture_il) {
|
||||
skip_hidden = layer_cur;
|
||||
}
|
||||
};
|
||||
|
||||
auto add_pos = [&](ggml_tensor * x, const clip_layer &) {
|
||||
return ggml_rope_ext(ctx0, x, inp_pos, nullptr, d_head,
|
||||
GGML_ROPE_TYPE_NEOX, 0, hparams.rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
|
||||
};
|
||||
|
||||
inpL = build_vit(inpL, n_pos, NORM_TYPE_NORMAL, hparams.ffn_op, nullptr, add_pos, opts);
|
||||
inpL = ggml_reshape_2d(ctx0, inpL, n_embd, n_pos); // build_vit restores a (size-1) batch dim
|
||||
|
||||
GGML_ASSERT(skip_hidden != nullptr);
|
||||
inpL = ggml_add(ctx0, inpL, skip_hidden);
|
||||
|
||||
inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);
|
||||
cb(inpL, "after_transformer", -1);
|
||||
|
||||
// downsample: strided conv (no bias) + gelu + layernorm
|
||||
{
|
||||
ggml_tensor * ds = ggml_cont(ctx0, ggml_transpose(ctx0, inpL)); // [n_pos, n_embd]
|
||||
ds = ggml_conv_1d(ctx0, model.downsample_conv_w, ds, 2, 0, 1);
|
||||
ds = ggml_gelu_erf(ctx0, ds);
|
||||
ds = ggml_cont(ctx0, ggml_transpose(ctx0, ds)); // [n_embd, n_pos/2]
|
||||
ds = build_norm(ds, model.downsample_norm_w, model.downsample_norm_b, NORM_TYPE_NORMAL, eps, -1);
|
||||
inpL = ds;
|
||||
}
|
||||
cb(inpL, "after_downsample", -1);
|
||||
|
||||
// RVQ quantize: codebook ne=[dim, max_bins, n_q]
|
||||
// quantize input vector to codes (type=I32)
|
||||
std::vector<ggml_tensor *> codes;
|
||||
{
|
||||
GGML_ASSERT(model.rvq_codebook != nullptr);
|
||||
const int64_t dim = model.rvq_codebook->ne[0];
|
||||
GGML_ASSERT(dim == inpL->ne[0]);
|
||||
GGML_ASSERT((int64_t) hparams.rvq_codebook_size.size() == model.rvq_codebook->ne[2]);
|
||||
|
||||
ggml_tensor * residual = inpL; // [dim, n_pos_ds]
|
||||
|
||||
for (size_t q = 0; q < hparams.rvq_codebook_size.size(); q++) {
|
||||
const int64_t bins = hparams.rvq_codebook_size[q];
|
||||
ggml_tensor * codebook_q = ggml_view_2d(ctx0, model.rvq_codebook, dim, bins,
|
||||
model.rvq_codebook->nb[1], q * model.rvq_codebook->nb[2]);
|
||||
codebook_q = ggml_cont(ctx0, codebook_q);
|
||||
|
||||
ggml_tensor * codebook_norm = ggml_sum_rows(ctx0, ggml_sqr(ctx0, codebook_q)); // [1, bins]
|
||||
codebook_norm = ggml_cont(ctx0, ggml_transpose(ctx0, codebook_norm)); // [bins, 1]
|
||||
|
||||
ggml_tensor * dot = ggml_mul_mat(ctx0, codebook_q, residual); // [bins, n_pos_ds]
|
||||
ggml_tensor * scores = ggml_sub(ctx0, ggml_scale(ctx0, dot, 2.0f), codebook_norm);
|
||||
|
||||
ggml_tensor * idx = ggml_argmax(ctx0, scores); // [n_pos_ds]
|
||||
codes.push_back(idx);
|
||||
|
||||
ggml_tensor * quant = ggml_get_rows(ctx0, codebook_q, idx); // [dim, n_pos_ds]
|
||||
residual = ggml_sub(ctx0, residual, quant);
|
||||
cb(idx, "rvq_code", (int) q);
|
||||
}
|
||||
}
|
||||
|
||||
// convert codes to LLM embeddings
|
||||
ggml_tensor * code_embd_sum = nullptr;
|
||||
{
|
||||
GGML_ASSERT(model.mm_a_code_embd != nullptr);
|
||||
const int64_t dim = model.mm_a_code_embd->ne[0];
|
||||
const int64_t vocab = model.mm_a_code_embd->ne[1];
|
||||
GGML_ASSERT((int64_t) codes.size() == model.mm_a_code_embd->ne[2]);
|
||||
GGML_ASSERT(dim == inpL->ne[0]);
|
||||
|
||||
for (size_t i = 0; i < codes.size(); i++) {
|
||||
ggml_tensor * table_i = ggml_view_2d(ctx0, model.mm_a_code_embd, dim, vocab,
|
||||
model.mm_a_code_embd->nb[1], i * model.mm_a_code_embd->nb[2]);
|
||||
table_i = ggml_cont(ctx0, table_i);
|
||||
|
||||
ggml_tensor * embd_i = ggml_get_rows(ctx0, table_i, codes[i]); // [dim, n_pos_ds]
|
||||
code_embd_sum = code_embd_sum ? ggml_add(ctx0, code_embd_sum, embd_i) : embd_i;
|
||||
}
|
||||
cb(code_embd_sum, "code_embd_sum", -1);
|
||||
}
|
||||
|
||||
// input_local_transformer
|
||||
// groups of `group_size` consecutive downsampled frames are processed together, attending only within their own group.
|
||||
// Implemented as a block-diagonal mask + in-group-repeating positions
|
||||
// (rather than a real batch dim) - same technique as the encoder's masks above, and as gemma4a's / deepseekocr2's chunked attention.
|
||||
|
||||
// note: hand-rolled here instead of build_vit() because this is a second, independent layer stack
|
||||
// (own layer array/count, RMSNorm instead of LN, SiLU FFN, own RoPE theta)
|
||||
|
||||
ggml_tensor * projected;
|
||||
{
|
||||
const int group_size = hparams.audio_local_group_size;
|
||||
GGML_ASSERT(group_size > 0);
|
||||
const int64_t n_pos_ds = code_embd_sum->ne[1];
|
||||
const int64_t n_groups = (n_pos_ds + group_size - 1) / group_size;
|
||||
const int64_t n_padded = n_groups * group_size;
|
||||
|
||||
ggml_tensor * cur_local = code_embd_sum;
|
||||
if (n_padded != n_pos_ds) {
|
||||
cur_local = ggml_pad(ctx0, cur_local, 0, (int) (n_padded - n_pos_ds), 0, 0);
|
||||
}
|
||||
|
||||
ggml_tensor * local_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_padded);
|
||||
ggml_set_name(local_pos, "mimo_audio_local_positions");
|
||||
ggml_set_input(local_pos);
|
||||
|
||||
ggml_tensor * local_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_padded, n_padded);
|
||||
ggml_set_name(local_mask, "mimo_audio_local_mask");
|
||||
ggml_set_input(local_mask);
|
||||
|
||||
const float local_rope_theta = 640000.0f; // audio_config.rope_theta (differs from the encoder's)
|
||||
auto apply_local_rope = [&](ggml_tensor * x) {
|
||||
return ggml_rope_ext(ctx0, x, local_pos, nullptr, d_head,
|
||||
GGML_ROPE_TYPE_NEOX, 0, local_rope_theta, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
|
||||
};
|
||||
|
||||
for (int il = 0; il < hparams.audio_local_n_layer; il++) {
|
||||
auto & layer = model.mm_a_local_layers[il];
|
||||
|
||||
ggml_tensor * attn_in = build_norm(cur_local, layer.ln_1_w, nullptr, NORM_TYPE_RMS, eps, il);
|
||||
|
||||
ggml_tensor * Qcur = build_mm(layer.q_w, attn_in);
|
||||
if (layer.q_b) {
|
||||
Qcur = ggml_add(ctx0, Qcur, layer.q_b);
|
||||
}
|
||||
ggml_tensor * Kcur = build_mm(layer.k_w, attn_in);
|
||||
if (layer.k_b) {
|
||||
Kcur = ggml_add(ctx0, Kcur, layer.k_b);
|
||||
}
|
||||
ggml_tensor * Vcur = build_mm(layer.v_w, attn_in);
|
||||
if (layer.v_b) {
|
||||
Vcur = ggml_add(ctx0, Vcur, layer.v_b);
|
||||
}
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_padded);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_padded);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_padded);
|
||||
|
||||
Qcur = apply_local_rope(Qcur);
|
||||
Kcur = apply_local_rope(Kcur);
|
||||
|
||||
ggml_tensor * attn_out = build_attn(layer.o_w, nullptr, Qcur, Kcur, Vcur, local_mask, kq_scale, il);
|
||||
cur_local = ggml_add(ctx0, cur_local, attn_out);
|
||||
|
||||
ggml_tensor * ffn_in = build_norm(cur_local, layer.ln_2_w, nullptr, NORM_TYPE_RMS, eps, il);
|
||||
ggml_tensor * ffn_out = build_ffn(ffn_in,
|
||||
layer.ff_up_w, nullptr,
|
||||
layer.ff_gate_w, nullptr,
|
||||
layer.ff_down_w, nullptr,
|
||||
FFN_SILU, il);
|
||||
cur_local = ggml_add(ctx0, cur_local, ffn_out);
|
||||
}
|
||||
|
||||
cur_local = build_norm(cur_local, model.mm_a_local_norm_w, nullptr, NORM_TYPE_RMS, eps, -1);
|
||||
cb(cur_local, "after_local_transformer", -1);
|
||||
|
||||
// flatten each group of `group_size` frames into one (group_size*n_embd)-dim vector
|
||||
// (matching AudioProjection's flattened input)
|
||||
ggml_tensor * grouped = ggml_reshape_2d(ctx0, cur_local, n_embd * group_size, n_groups);
|
||||
|
||||
// AudioProjection: Linear (no bias) -> GELU -> Linear (no bias)
|
||||
projected = build_ffn(grouped,
|
||||
model.mm_1_w, nullptr,
|
||||
nullptr, nullptr,
|
||||
model.mm_2_w, nullptr,
|
||||
FFN_GELU_ERF, -1);
|
||||
cb(projected, "after_projection", -1);
|
||||
}
|
||||
|
||||
ggml_build_forward_expand(gf, projected);
|
||||
return gf;
|
||||
}
|
||||
|
|
@ -210,6 +210,11 @@ struct clip_graph_qwen3a : clip_graph {
|
|||
ggml_cgraph * build() override;
|
||||
};
|
||||
|
||||
struct clip_graph_mimo_audio : clip_graph {
|
||||
clip_graph_mimo_audio(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
};
|
||||
|
||||
struct clip_graph_kimik25 : clip_graph {
|
||||
clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
|
|
@ -217,6 +222,11 @@ struct clip_graph_kimik25 : clip_graph {
|
|||
ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
|
||||
};
|
||||
|
||||
struct clip_graph_parakeet : clip_graph {
|
||||
clip_graph_parakeet(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
};
|
||||
|
||||
struct clip_graph_exaone4_5 : clip_graph {
|
||||
clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
|
|
|
|||
421
tools/mtmd/models/parakeet.cpp
Normal file
421
tools/mtmd/models/parakeet.cpp
Normal file
|
|
@ -0,0 +1,421 @@
|
|||
#include "models.h"
|
||||
|
||||
static constexpr int PARAKEET_LOCAL_ATTN_THRESHOLD = 8192;
|
||||
static constexpr int PARAKEET_LOCAL_ATTN_WINDOW = 128;
|
||||
|
||||
// conv subsampling + conformer encoder
|
||||
ggml_cgraph * clip_graph_parakeet::build() {
|
||||
|
||||
// Conv subsampling
|
||||
ggml_tensor * inp = build_inp_raw(1);
|
||||
inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
|
||||
|
||||
// [freq, time, channels, batch]
|
||||
ggml_tensor * cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[0], inp, 2, 2, 1, 1, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[0]);
|
||||
cb(cur, "pre_conv_0", -1);
|
||||
|
||||
cur = ggml_relu(ctx0, cur);
|
||||
cb(cur, "pre_conv_0_relu", -1);
|
||||
|
||||
// [freq, time, channels, batch]
|
||||
cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[2], cur, 2, 2, 1, 1, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[2]);
|
||||
cb(cur, "pre_conv_2", -1);
|
||||
|
||||
// [freq, time, channels, batch]
|
||||
cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[3], cur, 1, 1, 0, 0, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[3]);
|
||||
cb(cur, "pre_conv_3", -1);
|
||||
|
||||
cur = ggml_relu(ctx0, cur);
|
||||
cb(cur, "pre_conv_3_relu", -1);
|
||||
|
||||
// [freq, time, channels, batch]
|
||||
cur = ggml_conv_2d_dw_direct(ctx0, model.pre_encode_conv_X_w[5], cur, 2, 2, 1, 1, 1, 1);
|
||||
cb(cur, "pre_conv_5_direct", -1);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[5]);
|
||||
cb(cur, "pre_conv_5", -1);
|
||||
|
||||
// [freq, time, channels, batch]
|
||||
cur = ggml_conv_2d(ctx0, model.pre_encode_conv_X_w[6], cur, 1, 1, 0, 0, 1, 1);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_conv_X_b[6]);
|
||||
cb(cur, "pre_conv_6", -1);
|
||||
|
||||
cur = ggml_relu(ctx0, cur);
|
||||
cb(cur, "pre_conv_6_relu", -1);
|
||||
|
||||
// [freq, time, chan]
|
||||
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
||||
// [freq, chan, time]
|
||||
cur = ggml_cont(ctx0, cur);
|
||||
|
||||
const int n_freq = cur->ne[0];
|
||||
const int n_chan = cur->ne[1];
|
||||
const int n_frames = cur->ne[2];
|
||||
|
||||
// [freq, time, chan, batch] -> [(freq * chan), time]
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_freq * n_chan, n_frames);
|
||||
|
||||
cur = build_mm(model.pre_encode_out_w, cur);
|
||||
cur = ggml_add(ctx0, cur, model.pre_encode_out_b);
|
||||
|
||||
ggml_set_name(cur, "pre_enc_out");
|
||||
|
||||
// Encoder
|
||||
|
||||
const auto & hparams = model.hparams;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_state = hparams.n_embd;
|
||||
const float fc_factor = 0.5f;
|
||||
|
||||
const int n_time = cur->ne[1];
|
||||
const bool local_attn = n_time > PARAKEET_LOCAL_ATTN_THRESHOLD;
|
||||
const int att_left = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1;
|
||||
const int att_right = local_attn ? PARAKEET_LOCAL_ATTN_WINDOW : n_time - 1;
|
||||
const int window_size = local_attn ? att_left + att_right + 1 : 2 * n_time - 1;
|
||||
const int d_half = n_state / 2;
|
||||
const int mask_dim = local_attn ? window_size : n_time;
|
||||
|
||||
// mask [key, n_time]
|
||||
struct ggml_tensor * attn_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, mask_dim, n_time);
|
||||
ggml_set_name(attn_mask, "attn_mask");
|
||||
ggml_set_input(attn_mask);
|
||||
|
||||
struct ggml_tensor * local_mask = nullptr;
|
||||
if (local_attn) {
|
||||
const int chunk = att_left + att_right;
|
||||
local_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, chunk + window_size - 1, chunk);
|
||||
ggml_set_name(local_mask, "local_mask");
|
||||
ggml_set_input(local_mask);
|
||||
}
|
||||
|
||||
struct ggml_tensor * pos_freqs = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, d_half);
|
||||
ggml_set_name(pos_freqs, "pos_freqs");
|
||||
ggml_set_input(pos_freqs);
|
||||
|
||||
struct ggml_tensor * rel_positions = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, window_size);
|
||||
ggml_set_name(rel_positions, "rel_positions");
|
||||
ggml_set_input(rel_positions);
|
||||
|
||||
struct ggml_tensor * freqs = ggml_repeat_4d(ctx0, pos_freqs, d_half, window_size, 1, 1);
|
||||
struct ggml_tensor * theta = ggml_mul(ctx0, freqs, rel_positions);
|
||||
|
||||
struct ggml_tensor * sin = ggml_reshape_3d(ctx0, ggml_sin(ctx0, theta), 1, d_half, window_size);
|
||||
struct ggml_tensor * cos = ggml_reshape_3d(ctx0, ggml_cos(ctx0, theta), 1, d_half, window_size);
|
||||
struct ggml_tensor * pos_emb = ggml_reshape_2d(ctx0, ggml_cont(ctx0, ggml_concat(ctx0, sin, cos, 0)), n_state, window_size);
|
||||
ggml_set_name(pos_emb, "pos_emb");
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
const auto & layer = model.layers[il];
|
||||
// FFN1
|
||||
{
|
||||
struct ggml_tensor * residual = cur;
|
||||
ggml_format_name(cur, "enc_%d_res", il);
|
||||
|
||||
// norm
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_w), layer.ff_norm_b);
|
||||
ggml_format_name(cur, "enc_%d_ffn_norm_1", il);
|
||||
|
||||
cur = build_ffn(cur, layer.ff_up_w, nullptr, nullptr, nullptr, layer.ff_down_w, nullptr, FFN_SILU, il);
|
||||
ggml_format_name(cur, "enc_%d_ffn_1", il);
|
||||
|
||||
cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, fc_factor));
|
||||
ggml_format_name(cur, "enc_%d_res_ffn", il);
|
||||
}
|
||||
|
||||
// self attention block using relative positional encoding from model.position_embedding.
|
||||
{
|
||||
// [feat, time_frames, 1, 1]
|
||||
struct ggml_tensor * residual = cur;
|
||||
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_1_w), layer.ln_1_b);
|
||||
ggml_format_name(cur, "enc_%d_attn_norm", il);
|
||||
|
||||
const int n_head = hparams.n_head;
|
||||
const int d_head = n_state / n_head;
|
||||
|
||||
// [feat, time_frames, 1, 1]
|
||||
struct ggml_tensor * Q_cur = build_mm(layer.q_w, cur);
|
||||
struct ggml_tensor * K_cur = build_mm(layer.k_w, cur);
|
||||
struct ggml_tensor * V_cur = build_mm(layer.v_w, cur);
|
||||
|
||||
// [d_head, n_heads, n_time, 1]
|
||||
Q_cur = ggml_reshape_3d(ctx0, Q_cur, d_head, n_head, n_time);
|
||||
K_cur = ggml_reshape_3d(ctx0, K_cur, d_head, n_head, n_time);
|
||||
V_cur = ggml_reshape_3d(ctx0, V_cur, d_head, n_head, n_time);
|
||||
|
||||
// [n_state, window_size]
|
||||
struct ggml_tensor * pos = build_mm(layer.linear_pos_w, pos_emb);
|
||||
// [feat, head, window_size, 1]
|
||||
pos = ggml_reshape_3d(ctx0, pos, d_head, n_head, pos_emb->ne[1]);
|
||||
// [feat, window_size, head, 1]
|
||||
pos = ggml_cont(ctx0, ggml_permute(ctx0, pos, 0, 2, 1, 3));
|
||||
ggml_format_name(pos, "enc_%d_attn_pos", il);
|
||||
|
||||
if (local_attn) {
|
||||
const int chunk = att_left + att_right;
|
||||
const int n_group = (n_time + chunk - 1) / chunk;
|
||||
const int n_time_padded = n_group * chunk;
|
||||
const int n_kv_chunk = chunk + window_size - 1;
|
||||
const int n_kv_dense = n_kv_chunk * n_group;
|
||||
const bool need_padding = n_time_padded > n_time;
|
||||
|
||||
Q_cur = ggml_cont(ctx0, ggml_permute(ctx0, Q_cur, 0, 2, 1, 3));
|
||||
K_cur = ggml_cont(ctx0, ggml_permute(ctx0, K_cur, 0, 2, 1, 3));
|
||||
V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 0, 2, 1, 3));
|
||||
|
||||
// content bias
|
||||
struct ggml_tensor * bias_u = ggml_reshape_3d(ctx0, layer.pos_bias_u, d_head, 1, n_head);
|
||||
struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, bias_u);
|
||||
|
||||
// position bias
|
||||
struct ggml_tensor * bias_v = ggml_reshape_3d(ctx0, layer.pos_bias_v, d_head, 1, n_head);
|
||||
struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, bias_v);
|
||||
|
||||
// right pad the time dimension
|
||||
struct ggml_tensor * Q_u_padded = need_padding ?
|
||||
ggml_pad_ext(ctx0, Q_u, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : Q_u;
|
||||
Q_u_padded = ggml_reshape_4d(ctx0, Q_u_padded, d_head, chunk, n_group, n_head);
|
||||
|
||||
// pad front and back for the first and last time frames
|
||||
struct ggml_tensor * K_padded = ggml_pad_ext(ctx0, K_cur, 0, 0, att_left, att_right, 0, 0, 0, 0);
|
||||
if (n_kv_dense > K_padded->ne[1]) {
|
||||
K_padded = ggml_pad_ext(ctx0, K_padded, 0, 0, 0, n_kv_dense - K_padded->ne[1], 0, 0, 0, 0);
|
||||
}
|
||||
|
||||
// sliding window view: each group spans n_kv_chunk keys but steps by chunk
|
||||
struct ggml_tensor * K_chunk = ggml_view_4d(ctx0, K_padded,
|
||||
d_head, n_kv_chunk, n_group, n_head,
|
||||
K_padded->nb[1],
|
||||
(size_t) chunk * K_padded->nb[1],
|
||||
K_padded->nb[2],
|
||||
0);
|
||||
K_chunk = ggml_cont(ctx0, K_chunk);
|
||||
|
||||
struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_chunk, Q_u_padded);
|
||||
|
||||
// trim the dense output down to window_size scores per query
|
||||
content_scores = ggml_view_4d(ctx0, content_scores,
|
||||
window_size, chunk, n_group, n_head,
|
||||
(size_t) (chunk + window_size) * content_scores->nb[0],
|
||||
content_scores->nb[2],
|
||||
content_scores->nb[3],
|
||||
0);
|
||||
content_scores = ggml_cont(ctx0, content_scores);
|
||||
|
||||
// ungroup: [window_size, n_time_padded, n_head]
|
||||
content_scores = ggml_reshape_3d(ctx0, content_scores, window_size, n_time_padded, n_head);
|
||||
if (need_padding) {
|
||||
content_scores = ggml_view_3d(ctx0, content_scores,
|
||||
window_size, n_time, n_head,
|
||||
content_scores->nb[1],
|
||||
content_scores->nb[2],
|
||||
0);
|
||||
}
|
||||
|
||||
// Q_v: [d_head, time, head]
|
||||
Q_v = ggml_cont(ctx0, ggml_permute(ctx0, Q_v, 0, 2, 1, 3));
|
||||
struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v);
|
||||
|
||||
struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores);
|
||||
attn_scores = ggml_soft_max_ext(ctx0, attn_scores, attn_mask, 1.0f / std::sqrt(d_head), 0.0f);
|
||||
ggml_format_name(attn_scores, "enc_%d_attn_probs", il);
|
||||
|
||||
// expand probs back to n_kv_chunk width for the V matmul
|
||||
struct ggml_tensor * probs_padded = need_padding ?
|
||||
ggml_pad_ext(ctx0, attn_scores, 0, 0, 0, n_time_padded - n_time, 0, 0, 0, 0) : attn_scores;
|
||||
|
||||
probs_padded = ggml_reshape_4d(ctx0, probs_padded, window_size, chunk, n_group, n_head);
|
||||
probs_padded = ggml_pad_ext(ctx0, probs_padded, 0, chunk, 0, 0, 0, 0, 0, 0);
|
||||
probs_padded = ggml_view_4d(ctx0, probs_padded,
|
||||
n_kv_chunk, chunk, n_group, n_head,
|
||||
(size_t) n_kv_chunk * probs_padded->nb[0],
|
||||
probs_padded->nb[2],
|
||||
probs_padded->nb[3],
|
||||
0);
|
||||
probs_padded = ggml_cont(ctx0, probs_padded);
|
||||
probs_padded = ggml_mul(ctx0, probs_padded, local_mask);
|
||||
|
||||
struct ggml_tensor * V_padded = ggml_pad_ext(ctx0, V_cur, 0, 0, att_left, att_right, 0, 0, 0, 0);
|
||||
if (n_kv_dense > V_padded->ne[1]) {
|
||||
V_padded = ggml_pad_ext(ctx0, V_padded, 0, 0, 0, n_kv_dense - V_padded->ne[1], 0, 0, 0, 0);
|
||||
}
|
||||
V_padded = ggml_cont(ctx0, ggml_transpose(ctx0, V_padded));
|
||||
|
||||
struct ggml_tensor * V_chunk = ggml_view_4d(ctx0, V_padded,
|
||||
n_kv_chunk, d_head, n_group, n_head,
|
||||
V_padded->nb[1],
|
||||
(size_t) chunk * V_padded->nb[0],
|
||||
V_padded->nb[2],
|
||||
0);
|
||||
V_chunk = ggml_cont(ctx0, V_chunk);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, V_chunk, probs_padded);
|
||||
cur = ggml_reshape_3d(ctx0, cur, d_head, n_time_padded, n_head);
|
||||
if (need_padding) {
|
||||
cur = ggml_view_3d(ctx0, cur, d_head, n_time, n_head, cur->nb[1], cur->nb[2], 0);
|
||||
}
|
||||
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
|
||||
cur = ggml_reshape_2d(ctx0, cur, n_state, n_time);
|
||||
cur = build_mm(layer.o_w, cur);
|
||||
} else {
|
||||
// full attention
|
||||
struct ggml_tensor * Q_u = ggml_add(ctx0, Q_cur, layer.pos_bias_u);
|
||||
ggml_format_name(Q_u, "enc_%d_attn_q_u", il);
|
||||
|
||||
struct ggml_tensor * K_prep = ggml_permute(ctx0, K_cur, 0, 2, 1, 3);
|
||||
struct ggml_tensor * Q_prep = ggml_permute(ctx0, Q_u, 0, 2, 1, 3);
|
||||
struct ggml_tensor * content_scores = ggml_mul_mat(ctx0, K_prep, Q_prep);
|
||||
ggml_format_name(content_scores, "enc_%d_attn_content_scores", il);
|
||||
|
||||
struct ggml_tensor * Q_v = ggml_add(ctx0, Q_cur, layer.pos_bias_v);
|
||||
ggml_format_name(Q_v, "enc_%d_attn_q_v", il);
|
||||
|
||||
Q_v = ggml_permute(ctx0, Q_v, 0, 2, 1, 3);
|
||||
Q_v = ggml_cont(ctx0, Q_v);
|
||||
ggml_format_name(Q_v, "enc_%d_attn_q_v_perm", il);
|
||||
|
||||
struct ggml_tensor * rel_pos_scores = ggml_mul_mat(ctx0, pos, Q_v);
|
||||
ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos", il);
|
||||
|
||||
// Relative positional shift
|
||||
{
|
||||
const auto pos_window = rel_pos_scores->ne[0];
|
||||
const auto n_frame = rel_pos_scores->ne[1];
|
||||
const auto n_head = rel_pos_scores->ne[2];
|
||||
|
||||
rel_pos_scores = ggml_pad(ctx0, rel_pos_scores, 1, 0, 0, 0);
|
||||
rel_pos_scores = ggml_roll(ctx0, rel_pos_scores, 1, 0, 0, 0);
|
||||
|
||||
rel_pos_scores = ggml_reshape_3d(ctx0, rel_pos_scores, n_frame, pos_window + 1, n_head);
|
||||
rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
|
||||
ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_reshaped", il);
|
||||
|
||||
int center = pos_window / 2;
|
||||
size_t offset = rel_pos_scores->nb[0] * (center+1);
|
||||
|
||||
rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores,
|
||||
n_frame, pos_window, n_head,
|
||||
(pos_window) * 4,
|
||||
rel_pos_scores->nb[2],
|
||||
offset);
|
||||
rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
|
||||
ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted", il);
|
||||
|
||||
rel_pos_scores = ggml_view_3d(ctx0, rel_pos_scores,
|
||||
content_scores->ne[0],
|
||||
content_scores->ne[1],
|
||||
rel_pos_scores->ne[2],
|
||||
rel_pos_scores->nb[1],
|
||||
rel_pos_scores->nb[2],
|
||||
0);
|
||||
rel_pos_scores = ggml_cont(ctx0, rel_pos_scores);
|
||||
ggml_format_name(rel_pos_scores, "enc_%d_attn_rel_pos_shifted_view", il);
|
||||
}
|
||||
|
||||
struct ggml_tensor * attn_scores = ggml_add(ctx0, content_scores, rel_pos_scores);
|
||||
ggml_format_name(attn_scores, "enc_%d_attn_scores", il);
|
||||
attn_scores = ggml_scale(ctx0, attn_scores, 1.0f / std::sqrt(d_head));
|
||||
attn_scores = ggml_add(ctx0, attn_scores, attn_mask);
|
||||
ggml_format_name(attn_scores, "enc_%d_attn_scores_scaled", il);
|
||||
|
||||
struct ggml_tensor * probs = ggml_soft_max(ctx0, attn_scores);
|
||||
ggml_format_name(probs, "enc_%d_attn_probs", il);
|
||||
|
||||
V_cur = ggml_cont(ctx0, ggml_permute(ctx0, V_cur, 1, 2, 0, 3));
|
||||
ggml_format_name(V_cur, "enc_%d_attn_v_cur", il);
|
||||
cur = ggml_mul_mat(ctx0, probs, V_cur);
|
||||
ggml_format_name(cur, "enc_%d_attn_inp", il);
|
||||
|
||||
cur = ggml_permute(ctx0, cur, 2, 0, 1, 3);
|
||||
cur = ggml_cont_2d(ctx0, cur, n_state, n_time);
|
||||
cur = build_mm(layer.o_w, cur);
|
||||
}
|
||||
ggml_format_name(cur, "enc_%d_attn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, residual, cur);
|
||||
ggml_format_name(cur, "enc_%d_attn_res", il);
|
||||
}
|
||||
|
||||
// Convolution
|
||||
{
|
||||
struct ggml_tensor * residual = cur;
|
||||
ggml_format_name(cur, "enc_%d_residual_conv", il);
|
||||
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.norm_conv_w), layer.norm_conv_b);
|
||||
ggml_format_name(cur, "enc_%d_norm_conv", il);
|
||||
|
||||
// pointwise 1d convolution:
|
||||
cur = build_mm(layer.conv_pw1_w, cur);
|
||||
ggml_format_name(cur, "enc_%d_conv_pw1", il);
|
||||
|
||||
{
|
||||
int64_t d = cur->ne[0] / 2;
|
||||
struct ggml_tensor * signal = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], 0);
|
||||
struct ggml_tensor * gate = ggml_view_2d(ctx0, cur, d, cur->ne[1], cur->nb[1], d * cur->nb[0]);
|
||||
|
||||
cur = ggml_mul(ctx0, signal, ggml_sigmoid(ctx0, gate));
|
||||
ggml_format_name(cur, "enc_%d_conv_glu", il);
|
||||
}
|
||||
|
||||
cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
|
||||
|
||||
// use ggml_ssm_conv for f32 precision
|
||||
const int dw_pad = (hparams.audio_conv_kernel_size - 1) / 2;
|
||||
cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0);
|
||||
cur = ggml_roll(ctx0, cur, dw_pad, 0, 0, 0);
|
||||
cur = ggml_pad(ctx0, cur, dw_pad, 0, 0, 0);
|
||||
ggml_format_name(cur, "enc_%d_conv_dw_pad", il);
|
||||
|
||||
cur = ggml_ssm_conv(ctx0, cur, layer.conv_dw_w);
|
||||
ggml_format_name(cur, "enc_%d_conv_1d_dw", il);
|
||||
|
||||
cur = ggml_sub(ctx0, cur, layer.conv_norm_mean);
|
||||
struct ggml_tensor * std = ggml_sqrt(ctx0, layer.conv_norm_var);
|
||||
cur = ggml_div(ctx0, cur, std);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.conv_norm_w), layer.conv_norm_b);
|
||||
ggml_format_name(cur, "enc_%d_conv_bn", il);
|
||||
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
ggml_format_name(cur, "enc_%d_conv_silu", il);
|
||||
|
||||
cur = build_mm(layer.conv_pw2_w, cur);
|
||||
ggml_format_name(cur, "enc_%d_conv_pw2", il);
|
||||
|
||||
cur = ggml_add(ctx0, residual, cur);
|
||||
ggml_format_name(cur, "enc_%d_conv_res", il);
|
||||
}
|
||||
|
||||
// FFN2
|
||||
{
|
||||
struct ggml_tensor * residual = cur;
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ff_norm_1_w), layer.ff_norm_1_b);
|
||||
ggml_format_name(cur, "enc_%d_ffn_norm_2", il);
|
||||
|
||||
cur = build_ffn(cur, layer.ff_up_1_w, nullptr, nullptr, nullptr, layer.ff_down_1_w, nullptr, FFN_SILU, il);
|
||||
cur = ggml_add(ctx0, residual, ggml_scale(ctx0, cur, 0.5));
|
||||
ggml_format_name(cur, "enc_%d_ffn_res", il);
|
||||
}
|
||||
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.ln_2_w), layer.ln_2_b);
|
||||
}
|
||||
|
||||
cb(cur, "encoder_out", -1);
|
||||
|
||||
cur = ggml_rms_norm(ctx0, cur, 1e-6);
|
||||
cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
|
||||
cb(cur, "sound_projection.norm", -1);
|
||||
|
||||
cur = build_ffn(cur, model.mm_0_w, model.mm_0_b, nullptr, nullptr, model.mm_1_w, model.mm_1_b, FFN_RELU_SQR, -1);
|
||||
cb(cur, "projected", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
|
@ -725,6 +725,72 @@ bool mtmd_audio_preprocessor_qwen3a::preprocess(const float * sa
|
|||
return true;
|
||||
}
|
||||
|
||||
//
|
||||
// mtmd_audio_preprocessor_mimo_audio
|
||||
//
|
||||
// Matches torchaudio.transforms.MelSpectrogram(power=1.0, center=True) followed by
|
||||
// log(clip(spec, min=1e-7)): HTK mel scale, no Slaney area norm, magnitude (not power)
|
||||
// spectrogram, natural log, reflect-padded by n_fft/2 on each side.
|
||||
//
|
||||
|
||||
void mtmd_audio_preprocessor_mimo_audio::initialize() {
|
||||
cache.fill_sin_cos_table(hparams.audio_n_fft);
|
||||
cache.fill_hann_window(hparams.audio_window_len, true);
|
||||
cache.fill_mel_filterbank_matrix(
|
||||
hparams.n_mel_bins, hparams.audio_n_fft, hparams.audio_sample_rate,
|
||||
0.0f, hparams.audio_sample_rate / 2.0f,
|
||||
/*slaney_area_norm=*/ false,
|
||||
/*scale=*/ 1.0f,
|
||||
/*use_htk=*/ true
|
||||
);
|
||||
}
|
||||
|
||||
bool mtmd_audio_preprocessor_mimo_audio::preprocess(const float * samples,
|
||||
size_t n_samples,
|
||||
std::vector<mtmd_audio_mel> & output) {
|
||||
if (n_samples == 0) {
|
||||
return false;
|
||||
}
|
||||
|
||||
GGML_ASSERT(!cache.sin_vals.empty());
|
||||
GGML_ASSERT(!cache.cos_vals.empty());
|
||||
GGML_ASSERT(!cache.filters.data.empty());
|
||||
|
||||
const int pad = hparams.audio_n_fft / 2;
|
||||
|
||||
std::vector<float> padded(n_samples + 2 * pad, 0.0f);
|
||||
for (int i = 0; i < pad; i++) {
|
||||
int src = pad - i;
|
||||
padded[i] = (src < (int)n_samples) ? samples[src] : 0.0f;
|
||||
}
|
||||
std::copy(samples, samples + n_samples, padded.begin() + pad);
|
||||
for (int i = 0; i < pad; i++) {
|
||||
int src = (int)n_samples - 2 - i;
|
||||
padded[n_samples + pad + i] = (src >= 0) ? samples[src] : 0.0f;
|
||||
}
|
||||
|
||||
filter_params params;
|
||||
params.n_mel = hparams.n_mel_bins;
|
||||
params.n_fft_bins = 1 + (hparams.audio_n_fft / 2);
|
||||
params.hann_window_size = hparams.audio_window_len;
|
||||
params.hop_length = hparams.audio_hop_len;
|
||||
params.sample_rate = hparams.audio_sample_rate;
|
||||
params.no_padding = true; // reflect padding already applied above
|
||||
params.use_natural_log = true;
|
||||
params.use_magnitude = true;
|
||||
params.mel_floor = 1e-7f;
|
||||
params.norm_per_feature = false;
|
||||
|
||||
mtmd_audio_mel out;
|
||||
bool ok = log_mel_spectrogram(padded.data(), (int)padded.size(), 4, params, cache, out);
|
||||
if (!ok) {
|
||||
return false;
|
||||
}
|
||||
|
||||
output.push_back(std::move(out));
|
||||
return true;
|
||||
}
|
||||
|
||||
//
|
||||
// mtmd_audio_preprocessor_conformer
|
||||
//
|
||||
|
|
@ -956,6 +1022,209 @@ bool mtmd_audio_preprocessor_gemma4a::preprocess(const float * s
|
|||
}
|
||||
|
||||
//
|
||||
// mtmd_audio_preprocessor_parakeet implementation
|
||||
//
|
||||
|
||||
void mtmd_audio_preprocessor_parakeet::worker_thread(
|
||||
int ith,
|
||||
const float * window_func,
|
||||
int window_size,
|
||||
const std::vector<float> & samples,
|
||||
int n_samples,
|
||||
int frame_size,
|
||||
int frame_step,
|
||||
int n_threads,
|
||||
int n_fft_bins,
|
||||
const mtmd_audio_cache & cache,
|
||||
mtmd_audio_mel & mel) {
|
||||
std::vector<float> fft_in(frame_size * 2, 0.0);
|
||||
std::vector<float> fft_out(frame_size * 2 * 2 * 2);
|
||||
|
||||
int n_fb = n_fft_bins;
|
||||
int i = ith;
|
||||
|
||||
GGML_ASSERT(n_fb == 1 + (frame_size / 2));
|
||||
|
||||
const double eps = 5.960464477539063e-08;
|
||||
|
||||
for (; i < std::min(n_samples / frame_step + 1, (int) mel.n_len); i += n_threads) {
|
||||
const int offset = i * frame_step;
|
||||
const int window_pad_left = (frame_size - window_size) / 2;
|
||||
|
||||
// Zero-pad left.
|
||||
std::fill(fft_in.begin(), fft_in.begin() + window_pad_left, 0.0f);
|
||||
|
||||
// Apply windowed samples in the center.
|
||||
const int n_to_process = std::min({window_size, n_samples - offset});
|
||||
for (int j = 0; j < n_to_process; j++) {
|
||||
fft_in[window_pad_left + j] = window_func[j] * samples[offset + window_pad_left + j];
|
||||
}
|
||||
|
||||
// Zero-pad right.
|
||||
std::fill(fft_in.begin() + window_pad_left + n_to_process, fft_in.begin() + frame_size, 0.0f);
|
||||
|
||||
// FFT.
|
||||
fft(cache, fft_in.data(), frame_size, fft_out.data());
|
||||
|
||||
// Calculate modulus^2 of complex numbers.
|
||||
for (int j = 0; j < n_fb; j++) {
|
||||
fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
|
||||
}
|
||||
|
||||
// mel spectrogram.
|
||||
for (int j = 0; j < mel.n_mel; j++) {
|
||||
double sum = 0.0;
|
||||
int k = 0;
|
||||
for (k = 0; k < n_fb - 3; k += 4) {
|
||||
sum +=
|
||||
fft_out[k + 0] * cache.filters.data[j * n_fb + k + 0] +
|
||||
fft_out[k + 1] * cache.filters.data[j * n_fb + k + 1] +
|
||||
fft_out[k + 2] * cache.filters.data[j * n_fb + k + 2] +
|
||||
fft_out[k + 3] * cache.filters.data[j * n_fb + k + 3];
|
||||
}
|
||||
for (; k < n_fb; k++) {
|
||||
sum += fft_out[k] * cache.filters.data[j * n_fb + k];
|
||||
}
|
||||
mel.data[j * mel.n_len + i] = std::log(sum + eps);
|
||||
}
|
||||
}
|
||||
|
||||
// Otherwise fft_out are all zero.
|
||||
const double empty_sum = std::log(eps);
|
||||
for (; i < mel.n_len; i += n_threads) {
|
||||
for (int j = 0; j < mel.n_mel; j++) {
|
||||
mel.data[j * mel.n_len + i] = empty_sum;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void mtmd_audio_preprocessor_parakeet::initialize() {
|
||||
cache.fill_sin_cos_table(hparams.audio_n_fft);
|
||||
|
||||
const size_t n_fft = hparams.audio_n_fft / 2 + 1;
|
||||
GGML_ASSERT(hparams.mel_filters.size() == (size_t)hparams.n_mel_bins * n_fft);
|
||||
cache.filters.n_mel = hparams.n_mel_bins;
|
||||
cache.filters.n_fft = n_fft;
|
||||
cache.filters.data = hparams.mel_filters;
|
||||
|
||||
GGML_ASSERT(hparams.window.size() == (size_t)hparams.audio_window_len);
|
||||
GGML_ASSERT(hparams.window.size() <= (size_t) hparams.audio_n_fft);
|
||||
cache.hann_window = hparams.window;
|
||||
}
|
||||
|
||||
bool mtmd_audio_preprocessor_parakeet::preprocess(const float * samples,
|
||||
size_t n_samples_in,
|
||||
std::vector<mtmd_audio_mel> & output) {
|
||||
if (n_samples_in == 0) {
|
||||
return false;
|
||||
}
|
||||
|
||||
filter_params params;
|
||||
params.n_mel = hparams.n_mel_bins;
|
||||
params.n_fft_bins = 1 + (hparams.audio_n_fft / 2);
|
||||
params.hann_window_size = hparams.audio_window_len;
|
||||
params.hop_length = hparams.audio_hop_len;
|
||||
params.sample_rate = hparams.audio_sample_rate;
|
||||
|
||||
GGML_ASSERT(!cache.sin_vals.empty());
|
||||
GGML_ASSERT(!cache.cos_vals.empty());
|
||||
GGML_ASSERT(!cache.filters.data.empty());
|
||||
|
||||
const float * window_func = cache.hann_window.data();
|
||||
const int window_size = params.hann_window_size;
|
||||
const int frame_size = (params.n_fft_bins - 1) * 2;
|
||||
const int frame_step = params.hop_length;
|
||||
|
||||
// Apply preemphasis filter (high-pass): x[i] = x[i] - 0.97 * x[i-1]
|
||||
std::vector<float> samples_preprocessed(samples, samples + n_samples_in);
|
||||
{
|
||||
const float preemph = 0.97f;
|
||||
for (int i = n_samples_in - 1; i > 0; i--) {
|
||||
samples_preprocessed[i] = samples_preprocessed[i] - preemph * samples_preprocessed[i - 1];
|
||||
}
|
||||
}
|
||||
|
||||
// Parakeet uses centered constant padding
|
||||
const size_t pad = (size_t)(frame_size / 2);
|
||||
std::vector<float> samples_padded(n_samples_in + 2 * pad, 0.0f);
|
||||
std::copy(samples_preprocessed.begin(), samples_preprocessed.end(), samples_padded.begin() + pad);
|
||||
|
||||
mtmd_audio_mel out_full;
|
||||
out_full.n_mel = params.n_mel;
|
||||
out_full.n_len = (samples_padded.size() - frame_size) / frame_step + 1;
|
||||
out_full.n_len_org = out_full.n_len;
|
||||
out_full.data.resize(out_full.n_mel * out_full.n_len);
|
||||
|
||||
const int n_threads = 4;
|
||||
std::vector<std::thread> workers(n_threads - 1);
|
||||
for (int iw = 0; iw < n_threads - 1; ++iw) {
|
||||
workers[iw] = std::thread(
|
||||
worker_thread, iw + 1,
|
||||
window_func,
|
||||
window_size,
|
||||
std::cref(samples_padded),
|
||||
samples_padded.size(),
|
||||
frame_size,
|
||||
frame_step,
|
||||
n_threads,
|
||||
params.n_fft_bins,
|
||||
std::cref(cache),
|
||||
std::ref(out_full)
|
||||
);
|
||||
}
|
||||
|
||||
worker_thread(0,
|
||||
window_func,
|
||||
window_size,
|
||||
samples_padded,
|
||||
samples_padded.size(),
|
||||
frame_size,
|
||||
frame_step,
|
||||
n_threads,
|
||||
params.n_fft_bins,
|
||||
cache,
|
||||
out_full);
|
||||
|
||||
for (int iw = 0; iw < n_threads - 1; ++iw) {
|
||||
workers[iw].join();
|
||||
}
|
||||
|
||||
// Per-feature normalization (only on valid frames)
|
||||
{
|
||||
const double eps = 1e-5;
|
||||
int valid_frames = n_samples_in / frame_step;
|
||||
|
||||
for (int j = 0; j < out_full.n_mel; j++) {
|
||||
double sum = 0.0;
|
||||
double sq_diff_sum = 0.0;
|
||||
|
||||
// Calculate Mean ONLY on valid audio frames
|
||||
for (int i = 0; i < valid_frames; i++) {
|
||||
sum += (double)out_full.data[j * out_full.n_len + i];
|
||||
}
|
||||
double mean = sum / valid_frames;
|
||||
|
||||
// Calculate Variance ONLY on valid audio frames
|
||||
for (int i = 0; i < valid_frames; i++) {
|
||||
double diff = (double)out_full.data[j * out_full.n_len + i] - mean;
|
||||
sq_diff_sum += diff * diff;
|
||||
}
|
||||
|
||||
double std_dev = std::sqrt(sq_diff_sum / (valid_frames - 1.0));
|
||||
double denominator = std_dev + eps;
|
||||
|
||||
// Apply to ALL frames (including the padded ones)
|
||||
for (int i = 0; i < out_full.n_len; i++) {
|
||||
out_full.data[j * out_full.n_len + i] = (float)((out_full.data[j * out_full.n_len + i] - mean) / denominator);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
output.push_back(std::move(out_full));
|
||||
return true;
|
||||
}
|
||||
|
||||
|
||||
// mtmd_audio_preprocessor_gemma4ua
|
||||
//
|
||||
|
||||
|
|
|
|||
|
|
@ -111,6 +111,30 @@ struct mtmd_audio_preprocessor_qwen3a : mtmd_audio_preprocessor {
|
|||
mtmd_audio_cache cache;
|
||||
};
|
||||
|
||||
struct mtmd_audio_preprocessor_mimo_audio : mtmd_audio_preprocessor {
|
||||
mtmd_audio_preprocessor_mimo_audio(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
|
||||
void initialize() override;
|
||||
bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
|
||||
|
||||
private:
|
||||
mtmd_audio_cache cache;
|
||||
};
|
||||
|
||||
struct mtmd_audio_preprocessor_parakeet : mtmd_audio_preprocessor {
|
||||
mtmd_audio_preprocessor_parakeet(clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) { }
|
||||
void initialize() override;
|
||||
bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
|
||||
|
||||
private:
|
||||
mtmd_audio_cache cache;
|
||||
|
||||
static void worker_thread(int ith, const float * window_func, int window_size,
|
||||
const std::vector<float> & samples, int n_samples,
|
||||
int frame_size, int frame_step, int n_threads,
|
||||
int n_fft_bins,
|
||||
const mtmd_audio_cache & cache, mtmd_audio_mel & mel);
|
||||
};
|
||||
|
||||
//
|
||||
// streaming ISTFT - converts spectrogram frames back to audio one frame at a time
|
||||
//
|
||||
|
|
|
|||
|
|
@ -724,12 +724,22 @@ struct mtmd_context {
|
|||
aud_end = "<audio|>";
|
||||
audio_preproc = std::make_unique<mtmd_audio_preprocessor_gemma4a>(ctx_a);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_PARAKEET:
|
||||
{
|
||||
audio_preproc = std::make_unique<mtmd_audio_preprocessor_parakeet>(ctx_a);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GEMMA4UA:
|
||||
{
|
||||
aud_beg = "<|audio>";
|
||||
aud_end = "<audio|>";
|
||||
audio_preproc = std::make_unique<mtmd_audio_preprocessor_gemma4ua>(ctx_a);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MIMO_AUDIO:
|
||||
{
|
||||
aud_beg = "<|mimo_audio_start|>";
|
||||
aud_end = "<|mimo_audio_end|>";
|
||||
audio_preproc = std::make_unique<mtmd_audio_preprocessor_mimo_audio>(ctx_a);
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error(string_format("%s: unexpected audio projector type %d\n", __func__, proj));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -164,6 +164,8 @@ struct server_slot {
|
|||
llama_context * ctx_tgt = nullptr;
|
||||
llama_context * ctx_dft = nullptr;
|
||||
|
||||
common_memory mem;
|
||||
|
||||
// multimodal
|
||||
mtmd_context * mctx = nullptr;
|
||||
mtmd::batch_ptr mbatch = nullptr;
|
||||
|
|
@ -253,10 +255,7 @@ struct server_slot {
|
|||
void prompt_clear() {
|
||||
SLT_TRC(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
|
||||
|
||||
common_context_seq_rm(ctx_tgt, id, -1, -1);
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm(ctx_dft, id, -1, -1);
|
||||
}
|
||||
mem.seq_rm(id, -1, -1);
|
||||
|
||||
prompt.clear();
|
||||
}
|
||||
|
|
@ -668,13 +667,8 @@ struct server_slot {
|
|||
void copy_state_to(server_slot & other) const {
|
||||
GGML_ASSERT(state == SLOT_STATE_DONE_PROMPT);
|
||||
|
||||
common_context_seq_rm(ctx_tgt, other.id, -1, -1);
|
||||
common_context_seq_cp(ctx_tgt, id, other.id, -1, -1);
|
||||
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm(ctx_dft, other.id, -1, -1);
|
||||
common_context_seq_cp(ctx_dft, id, other.id, -1, -1);
|
||||
}
|
||||
mem.seq_rm(other.id, -1, -1);
|
||||
mem.seq_cp(id, other.id, -1, -1);
|
||||
|
||||
other.n_decoded = n_decoded;
|
||||
other.n_remaining = n_remaining;
|
||||
|
|
@ -1302,6 +1296,7 @@ private:
|
|||
slot.id = i;
|
||||
slot.ctx_tgt = ctx_tgt;
|
||||
slot.ctx_dft = ctx_dft;
|
||||
slot.mem.init(ctx_tgt, ctx_dft);
|
||||
slot.spec = spec.get();
|
||||
slot.n_ctx = n_ctx_slot;
|
||||
|
||||
|
|
@ -1542,7 +1537,7 @@ private:
|
|||
|
||||
// find the slot that has at least n% prompt similarity
|
||||
if (slot_prompt_similarity != 0.0f) {
|
||||
float sim_best = 0;
|
||||
float f_sim_best = 0;
|
||||
|
||||
for (server_slot & slot : slots) {
|
||||
if (task.id_slot != -1 && slot.id != task.id_slot) {
|
||||
|
|
@ -1551,6 +1546,7 @@ private:
|
|||
|
||||
// skip the slot if it is not available
|
||||
if (slot.is_processing()) {
|
||||
SLT_TRC(slot, " - skipping, is_processing = %d\n", slot.is_processing());
|
||||
continue;
|
||||
}
|
||||
|
||||
|
|
@ -1558,26 +1554,30 @@ private:
|
|||
|
||||
// skip the slot if it does not contains cached tokens
|
||||
if (tokens.empty()) {
|
||||
SLT_TRC(slot, "%s", " - skipping, slot is empty\n");
|
||||
continue;
|
||||
}
|
||||
|
||||
// fraction of the Longest Common Prefix length with respect to the input prompt length
|
||||
const float sim_cur = float(tokens.get_common_prefix(task.tokens)) / task.tokens.size();
|
||||
const size_t lcp_len = tokens.get_common_prefix(task.tokens);
|
||||
const float f_sim_cur = float(lcp_len) / task.tokens.size();
|
||||
|
||||
SLT_TRC(slot, " - checking sim = %.3f (%zu/%zu) > %.3f\n", f_sim_cur, lcp_len, task.tokens.size(), slot_prompt_similarity);
|
||||
|
||||
// select the current slot if the criteria match
|
||||
if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) {
|
||||
sim_best = sim_cur;
|
||||
if (f_sim_cur > f_sim_best && f_sim_cur > slot_prompt_similarity) {
|
||||
f_sim_best = f_sim_cur;
|
||||
|
||||
ret = &slot;
|
||||
}
|
||||
}
|
||||
|
||||
if (ret != nullptr) {
|
||||
const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size();
|
||||
const float f_keep = (f_sim_best*task.tokens.size()) / ret->prompt.tokens.size();
|
||||
|
||||
if (task.id_slot == -1) {
|
||||
SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
|
||||
sim_best, slot_prompt_similarity, f_keep);
|
||||
SLT_INF(*ret, "selected slot by LCP similarity, f_sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
|
||||
f_sim_best, slot_prompt_similarity, f_keep);
|
||||
}
|
||||
|
||||
// if we are about to lose a large portion of the existing context - save it in the prompt cache
|
||||
|
|
@ -2881,13 +2881,8 @@ private:
|
|||
|
||||
SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
|
||||
|
||||
common_context_seq_rm (ctx_tgt, slot.id, n_keep , n_keep + n_discard);
|
||||
common_context_seq_add(ctx_tgt, slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
|
||||
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm (ctx_dft, slot.id, n_keep , n_keep + n_discard);
|
||||
common_context_seq_add(ctx_dft, slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
|
||||
}
|
||||
slot.mem.seq_rm (slot.id, n_keep , n_keep + n_discard);
|
||||
slot.mem.seq_add(slot.id, n_keep + n_discard, slot.prompt.tokens.pos_next(), -n_discard);
|
||||
|
||||
// add generated tokens to cache
|
||||
// ref: https://github.com/ggml-org/llama.cpp/pull/16818#discussion_r2473269481
|
||||
|
|
@ -2998,7 +2993,9 @@ private:
|
|||
ckpt.load_dft(ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
|
||||
}
|
||||
|
||||
common_context_seq_rm(ctx_dft, slot.id, ckpt.pos_max + 1, -1);
|
||||
if (!llama_memory_seq_rm(llama_get_memory(ctx_dft), slot.id, ckpt.pos_max + 1, -1)) {
|
||||
GGML_ABORT("failed to remove sequence %d\n", slot.id);
|
||||
}
|
||||
}
|
||||
|
||||
if (!draft.empty()) {
|
||||
|
|
@ -3201,13 +3198,8 @@ private:
|
|||
|
||||
const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;
|
||||
|
||||
common_context_seq_rm (ctx_tgt, slot.id, head_p, head_c);
|
||||
common_context_seq_add(ctx_tgt, slot.id, head_c, head_c + n_match, kv_shift);
|
||||
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm (ctx_dft, slot.id, head_p, head_c);
|
||||
common_context_seq_add(ctx_dft, slot.id, head_c, head_c + n_match, kv_shift);
|
||||
}
|
||||
slot.mem.seq_rm (slot.id, head_p, head_c);
|
||||
slot.mem.seq_add(slot.id, head_c, head_c + n_match, kv_shift);
|
||||
|
||||
for (size_t i = 0; i < n_match; i++) {
|
||||
slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]);
|
||||
|
|
@ -3379,10 +3371,7 @@ private:
|
|||
|
||||
SLT_TRC(slot, "cached n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);
|
||||
|
||||
common_context_seq_rm(ctx_tgt, slot.id, p0, -1);
|
||||
if (ctx_dft) {
|
||||
common_context_seq_rm(ctx_dft, slot.id, p0, -1);
|
||||
}
|
||||
slot.mem.seq_rm(slot.id, p0, -1);
|
||||
|
||||
// If using an alora, there may be uncached tokens that come
|
||||
// before the invocation sequence. When this happens, the
|
||||
|
|
@ -3837,18 +3826,14 @@ private:
|
|||
|
||||
SLT_DBG(slot, "restoring speculative checkpoint (pos_min = %d, pos_max = %d, size = %zu)\n", ckpt.pos_min, ckpt.pos_max, ckpt.size());
|
||||
|
||||
{
|
||||
ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
|
||||
|
||||
common_context_seq_rm(slot.ctx_tgt, slot.id, ckpt.pos_max + 1, -1);
|
||||
}
|
||||
ckpt.load_tgt(slot.ctx_tgt, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
|
||||
|
||||
if (slot.ctx_dft) {
|
||||
ckpt.load_dft(slot.ctx_dft, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
|
||||
|
||||
common_context_seq_rm(slot.ctx_dft, slot.id, ckpt.pos_max + 1, -1);
|
||||
}
|
||||
|
||||
slot.mem.seq_rm(slot.id, ckpt.pos_max + 1, -1);
|
||||
|
||||
slot.prompt.tokens.keep_first(ckpt.n_tokens);
|
||||
slot.smpl = std::move(smpl_save);
|
||||
|
||||
|
|
@ -3889,10 +3874,7 @@ private:
|
|||
slot.sampled = ids.back(); // last accepted token
|
||||
SLT_DBG(slot, "add accepted tokens: sampled=%d, ids.size=%zu, n_draft=%zu\n", slot.sampled, ids.size(), n_draft);
|
||||
|
||||
common_context_seq_rm(slot.ctx_tgt, slot.id, slot.prompt.tokens.pos_next(), -1);
|
||||
if (slot.ctx_dft) {
|
||||
common_context_seq_rm(slot.ctx_dft, slot.id, slot.prompt.tokens.pos_next(), -1);
|
||||
}
|
||||
slot.mem.seq_rm(slot.id, slot.prompt.tokens.pos_next(), -1);
|
||||
|
||||
for (size_t i = 0; i < ids.size(); ++i) {
|
||||
completion_token_output result;
|
||||
|
|
|
|||
|
|
@ -209,6 +209,7 @@ std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params &
|
|||
->set_hard_limits(0.0f, 1.0f)
|
||||
->set_desc("Minimum speculative decoding probability for draft tokens (0 = greedy)"));
|
||||
|
||||
|
||||
add((new field_str("speculative.type"))
|
||||
->set_desc("Speculative decoding method (for debugging and research purposes)")
|
||||
->set_handler([&](field_eval_context & ctx, const json & data) {
|
||||
|
|
|
|||
|
|
@ -1742,9 +1742,9 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
|
|||
const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);
|
||||
|
||||
float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
|
||||
float sim_best = float(lcp_best) / tokens_new.size();
|
||||
float f_sim_best = float(lcp_best) / tokens_new.size();
|
||||
|
||||
SRV_TRC(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
||||
SRV_TRC(" - looking for better prompt, base f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best);
|
||||
|
||||
auto it_best = states.end();
|
||||
|
||||
|
|
@ -1753,23 +1753,25 @@ bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tok
|
|||
const int lcp_cur = it->prompt.tokens.get_common_prefix(tokens_new);
|
||||
|
||||
const float f_keep_cur = float(lcp_cur) / it->prompt.tokens.size();
|
||||
const float sim_cur = float(lcp_cur) / tokens_new.size();
|
||||
const float f_sim_cur = float(lcp_cur) / tokens_new.size();
|
||||
|
||||
SRV_TRC(" - prompt with length %7zu, lcp = %7d, f_keep = %.3f, f_sim = %.3f\n", it->prompt.tokens.size(), lcp_cur, f_keep_cur, f_sim_cur);
|
||||
|
||||
// don't trash large prompts
|
||||
if (f_keep_cur < 0.25f) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
|
||||
if (f_keep_best < f_keep_cur && f_sim_best < f_sim_cur) {
|
||||
f_keep_best = f_keep_cur;
|
||||
sim_best = sim_cur;
|
||||
f_sim_best = f_sim_cur;
|
||||
|
||||
it_best = it;
|
||||
}
|
||||
}
|
||||
|
||||
if (it_best != states.end()) {
|
||||
SRV_TRC(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
|
||||
SRV_TRC(" - found better prompt with f_keep = %.3f, f_sim = %.3f\n", f_keep_best, f_sim_best);
|
||||
|
||||
{
|
||||
auto & data = it_best->data.main;
|
||||
|
|
|
|||
|
|
@ -650,7 +650,7 @@ struct server_prompt_cache {
|
|||
|
||||
server_prompt_cache_state * alloc(const server_prompt & prompt, size_t state_size_main, size_t state_size_drft);
|
||||
|
||||
bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_main, llama_context * ctx_drft, int32_t id_slot);
|
||||
bool load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx_tgt, llama_context * ctx_dft, int32_t id_slot);
|
||||
|
||||
void update();
|
||||
};
|
||||
|
|
|
|||
|
|
@ -11,8 +11,7 @@
|
|||
classifyToolResult,
|
||||
formatJsonPretty,
|
||||
parseToolResultWithImages,
|
||||
type AgenticSection,
|
||||
type ToolResultLine
|
||||
type AgenticSection
|
||||
} from '$lib/utils';
|
||||
import { getBuiltinToolUi } from '$lib/constants/built-in-tools';
|
||||
import type { DatabaseMessageExtra } from '$lib/types';
|
||||
|
|
@ -29,11 +28,10 @@
|
|||
let { section, open, isStreaming, attachments, onToggle }: Props = $props();
|
||||
|
||||
const title = $derived(getBuiltinToolUi(section.toolName)?.label ?? section.toolName ?? '');
|
||||
|
||||
const parsedLines: ToolResultLine[] = $derived(
|
||||
const outputKind = $derived(classifyToolResult(section.toolResult));
|
||||
const parsedLines = $derived(
|
||||
section.toolResult ? parseToolResultWithImages(section.toolResult, attachments) : []
|
||||
);
|
||||
const outputKind = $derived(classifyToolResult(section.toolResult));
|
||||
</script>
|
||||
|
||||
<ToolCallBlock {section} {open} {isStreaming} meta={null} {title} {onToggle}>
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@
|
|||
let { section, open, isStreaming, onToggle }: Props = $props();
|
||||
|
||||
const editFileMeta = $derived(parseEditFileMeta(section));
|
||||
|
||||
const editDiffs = $derived(
|
||||
(editFileMeta?.edits ?? []).map((edit) => computeLineDiff(edit.oldText, edit.newText))
|
||||
);
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@
|
|||
const isStreamingCall = $derived(section.type === AgenticSectionType.TOOL_CALL_STREAMING);
|
||||
const showSpinner = $derived(isPending || (isStreamingCall && isStreaming));
|
||||
|
||||
const results: SearchResult[] = $derived(extractSearchResults(section.toolResult));
|
||||
const results = $derived(extractSearchResults(section.toolResult));
|
||||
const query = $derived(extractSearchQuery(section.toolArgs));
|
||||
|
||||
// Same icon-resolution chain as ChatMessageToolCallBlockDefault so
|
||||
|
|
|
|||
|
|
@ -28,6 +28,18 @@ export const LATEX_MATH_AND_CODE_PATTERN =
|
|||
/** Regex to capture the content of a $$...\\\\...$$ block (display-formula with line-break) */
|
||||
export const LATEX_LINEBREAK_REGEXP = /\$\$([\s\S]*?\\\\[\s\S]*?)\$\$/;
|
||||
|
||||
/**
|
||||
* Matches the unescaped `\[...\]` display-math delimiter and surrounding
|
||||
* context so callers can insert line-breaks around the placeholder or convert
|
||||
* to inline when the formula has a non-empty trailing context (e.g. a table
|
||||
* cell that opens with `\[` and closes with content after `\]`).
|
||||
*
|
||||
* group 1: prefix before `\[`
|
||||
* group 2: formula body
|
||||
* group 3: trailing context after `\]`
|
||||
*/
|
||||
export const LATEX_DISPLAY_BLOCK_REGEXP = /([\S].*?)\\\[([\s\S]*?)\\\](.*)/g;
|
||||
|
||||
/**
|
||||
* Cheap gate for `preprocessLaTeX`. Every transformation it performs is triggered
|
||||
* by a `$` (inline/display math, currency escaping) or a backslash escape
|
||||
|
|
@ -36,6 +48,76 @@ export const LATEX_LINEBREAK_REGEXP = /\$\$([\s\S]*?\\\\[\s\S]*?)\$\$/;
|
|||
*/
|
||||
export const LATEX_TRIGGER_REGEXP = /[$\\]/;
|
||||
|
||||
/** Inline LaTeX math delimiter (the dollar sign). */
|
||||
export const LATEX_INLINE_DELIMITER = '$';
|
||||
|
||||
/** Display LaTeX math delimiter (paired dollar signs). */
|
||||
export const LATEX_DISPLAY_DELIMITER = '$$';
|
||||
|
||||
/** Matches a single non-whitespace character. */
|
||||
export const LATEX_NON_WHITESPACE_REGEXP = /\S/;
|
||||
|
||||
/** Matches a character that may appear adjacent to `$`, indicating a non-TeX
|
||||
* context such as an identifier (`var$`, `$var`), currency ($5), or code. */
|
||||
export const LATEX_NEIGHBOR_CHAR_REGEXP = /[A-Za-z0-9_$-]/;
|
||||
|
||||
/** Matches a single digit (used to detect currency-like `$5`). */
|
||||
export const LATEX_DIGIT_REGEXP = /[0-9]/;
|
||||
|
||||
/** Matches the leading blockquote prefix (`> ` or `>`) on a markdown line. */
|
||||
export const LATEX_BLOCKQUOTE_PREFIX_REGEXP = /^(>\s*)/;
|
||||
|
||||
/** Matches the placeholder inserted by the protect/restore pipeline for a
|
||||
* protected LaTeX expression. Group 1 is the index into `latexExpressions`. */
|
||||
export const LATEX_PLACEHOLDER_REGEXP = /<<LATEX_(\d+)>>/g;
|
||||
|
||||
/** Matches the placeholder inserted by the protect/restore pipeline for a
|
||||
* protected code block. Group 1 is the index into `codeBlocks`. */
|
||||
export const CODE_BLOCK_PLACEHOLDER_REGEXP = /<<CODE_BLOCK_(\d+)>>/g;
|
||||
|
||||
/** Matches a `$` immediately followed by a digit, which is treated as a
|
||||
* currency amount (e.g. `$5`) and escaped to `\$5` so it isn't parsed as math. */
|
||||
export const LATEX_CURRENCY_DOLLAR_REGEXP = /\$(?=\d)/g;
|
||||
|
||||
/** Captures remaining `$$...$$`, `\[...\]`, `\(...\)` (only unescaped via
|
||||
* `(?<!\\)`) after the display-block pass has run. Group 1 holds the
|
||||
* matched formula. */
|
||||
export const LATEX_PROTECT_REGEXP =
|
||||
/(\$\$[\s\S]*?\$\$|(?<!\\)\\\[[\s\S]*?\\\]|(?<!\\)\\\(.*?\\\))/g;
|
||||
|
||||
/** Matches unescaped inline `\(...\)` (at least one char inside) used to
|
||||
* convert `\(` → `$` after the protect pass. */
|
||||
export const LATEX_INLINE_CONVERT_REGEXP = /(?<!\\)\\\((.+?)\\\)/g;
|
||||
|
||||
/** Matches unescaped display `\[...\]` used to convert `\[` → `$$`
|
||||
* after the protect pass. */
|
||||
export const LATEX_DISPLAY_CONVERT_REGEXP = /(?<!\\)\\\[([\s\S]*?)\\\]/g;
|
||||
|
||||
/** `\(` — opens an inline LaTeX math block. */
|
||||
export const LATEX_INLINE_OPEN = '\\(';
|
||||
|
||||
/** `\)` — closes an inline LaTeX math block. */
|
||||
export const LATEX_INLINE_CLOSE = '\\)';
|
||||
|
||||
/** `\[` — opens a display LaTeX math block. */
|
||||
export const LATEX_DISPLAY_OPEN = '\\[';
|
||||
|
||||
/** `\]` — closes a display LaTeX math block. */
|
||||
export const LATEX_DISPLAY_CLOSE = '\\]';
|
||||
|
||||
/** `\` — the LaTeX escape character. */
|
||||
export const LATEX_BACKSLASH = '\\';
|
||||
|
||||
/** `\$` — dollar sign escaped so it isn't parsed as math (used to disambiguate
|
||||
* currency amounts like `$5`). */
|
||||
export const LATEX_CURRENCY_ESCAPE = '\\$';
|
||||
|
||||
/** `\ce{` — mhchem chemistry command prefix. */
|
||||
export const LATEX_MHCHEM_CE = '\\ce{';
|
||||
|
||||
/** `\pu{` — mhchem physics-unit command prefix. */
|
||||
export const LATEX_MHCHEM_PU = '\\pu{';
|
||||
|
||||
/** map from mchem-regexp to replacement */
|
||||
export const MHCHEM_PATTERN_MAP: readonly [RegExp, string][] = [
|
||||
[/(\s)\$\\ce{/g, '$1$\\\\ce{'],
|
||||
|
|
|
|||
|
|
@ -92,8 +92,17 @@ function deriveSingleTurnSections(
|
|||
|
||||
// 3. Persisted tool calls (from message.toolCalls field)
|
||||
const toolCalls = parseToolCalls(message.toolCalls);
|
||||
|
||||
// Index tool messages by toolCallId for O(1) lookup instead of O(n) find()
|
||||
const toolMsgById = new Map<string, DatabaseMessage>();
|
||||
for (const tm of toolMessages) {
|
||||
if (tm.toolCallId && !toolMsgById.has(tm.toolCallId)) {
|
||||
toolMsgById.set(tm.toolCallId, tm);
|
||||
}
|
||||
}
|
||||
|
||||
for (const tc of toolCalls) {
|
||||
const resultMsg = toolMessages.find((m) => m.toolCallId === tc.id);
|
||||
const resultMsg = tc.id ? toolMsgById.get(tc.id) : undefined;
|
||||
// Only show as pending/loading if we're actively streaming; otherwise it's just a tool call without result
|
||||
const type = resultMsg
|
||||
? AgenticSectionType.TOOL_CALL
|
||||
|
|
@ -112,9 +121,10 @@ function deriveSingleTurnSections(
|
|||
}
|
||||
|
||||
// 4. Streaming tool calls (not yet persisted - currently being received)
|
||||
const persistedIds = new Set(toolCalls.map((t) => t.id).filter(Boolean));
|
||||
for (const tc of streamingToolCalls) {
|
||||
// Skip if already in persisted tool calls
|
||||
if (tc.id && toolCalls.find((t) => t.id === tc.id)) continue;
|
||||
if (tc.id && persistedIds.has(tc.id)) continue;
|
||||
sections.push({
|
||||
type: AgenticSectionType.TOOL_CALL_STREAMING,
|
||||
content: '',
|
||||
|
|
@ -281,15 +291,31 @@ export function splitSearchSummaryList(
|
|||
return { lines };
|
||||
}
|
||||
|
||||
/** Bounded cache for parseToolResultWithImages results. */
|
||||
const TOOL_RESULT_LINES_CACHE_MAX_SIZE = 32;
|
||||
const toolResultLinesCache = new Map<string, ToolResultLine[]>();
|
||||
|
||||
/**
|
||||
* Parse tool result text into lines, matching image attachments by name.
|
||||
* Memoized: called per render during streaming on unchanged tool result
|
||||
* strings with unchanged extras.
|
||||
*/
|
||||
export function parseToolResultWithImages(
|
||||
toolResult: string,
|
||||
extras?: DatabaseMessageExtra[]
|
||||
): ToolResultLine[] {
|
||||
// Cache key includes image attachment names so we recompute when
|
||||
// attachments change, even if the count stays the same.
|
||||
const imageNames = (extras ?? [])
|
||||
.filter((e): e is DatabaseMessageExtraImageFile => e.type === AttachmentType.IMAGE)
|
||||
.map((e) => e.name)
|
||||
.join(NEWLINE);
|
||||
const cacheKey = `${imageNames}:${toolResult}`;
|
||||
const cached = toolResultLinesCache.get(cacheKey);
|
||||
if (cached !== undefined) return cached;
|
||||
|
||||
const lines = toolResult.split(NEWLINE);
|
||||
return lines.map((line) => {
|
||||
const result = lines.map((line) => {
|
||||
const match = line.match(ATTACHMENT_SAVED_REGEX);
|
||||
if (!match || !extras) return { text: line };
|
||||
|
||||
|
|
@ -301,8 +327,19 @@ export function parseToolResultWithImages(
|
|||
|
||||
return { text: line, image };
|
||||
});
|
||||
|
||||
if (toolResultLinesCache.size >= TOOL_RESULT_LINES_CACHE_MAX_SIZE) {
|
||||
toolResultLinesCache.delete(toolResultLinesCache.keys().next().value!);
|
||||
}
|
||||
toolResultLinesCache.set(cacheKey, result);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Bounded cache for classifyToolResult results. */
|
||||
const CLASSIFY_CACHE_MAX_SIZE = 32;
|
||||
const classifyCache = new Map<string, ToolResultKind>();
|
||||
|
||||
/**
|
||||
* Pick a renderer tier for a tool's result content.
|
||||
*
|
||||
|
|
@ -312,25 +349,39 @@ export function parseToolResultWithImages(
|
|||
* through MarkdownContent for proper formatting.
|
||||
* text - everything else, rendered as plain text lines (with image
|
||||
* attachment resolution as a side effect).
|
||||
* Memoized: called per render during streaming on unchanged content.
|
||||
*/
|
||||
export function classifyToolResult(content: string | undefined): ToolResultKind {
|
||||
if (!content) return ToolResultKind.TEXT;
|
||||
|
||||
const cached = classifyCache.get(content);
|
||||
if (cached !== undefined) return cached;
|
||||
|
||||
const trimmed = content.trim();
|
||||
if (!trimmed) return ToolResultKind.TEXT;
|
||||
|
||||
let result: ToolResultKind = ToolResultKind.TEXT;
|
||||
|
||||
// Strongest signal: JSON object/array round-trips through JSON.parse.
|
||||
if (TOOL_RESULT_JSON_OPEN_REGEX.test(trimmed)) {
|
||||
try {
|
||||
JSON.parse(trimmed);
|
||||
return ToolResultKind.JSON;
|
||||
result = ToolResultKind.JSON;
|
||||
} catch (error) {
|
||||
console.error('[agentic] tool result looked like JSON but failed to parse:', error);
|
||||
}
|
||||
}
|
||||
|
||||
if (looksLikeMarkdown(trimmed)) return ToolResultKind.MARKDOWN;
|
||||
if (result === ToolResultKind.TEXT && looksLikeMarkdown(trimmed)) {
|
||||
result = ToolResultKind.MARKDOWN;
|
||||
}
|
||||
|
||||
return ToolResultKind.TEXT;
|
||||
if (classifyCache.size >= CLASSIFY_CACHE_MAX_SIZE) {
|
||||
classifyCache.delete(classifyCache.keys().next().value!);
|
||||
}
|
||||
classifyCache.set(content, result);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -370,19 +421,35 @@ function looksLikeMarkdown(content: string): boolean {
|
|||
return false;
|
||||
}
|
||||
|
||||
/** Bounded cache for parsed tool-call JSON blobs. */
|
||||
const TOOL_CALLS_CACHE_MAX_SIZE = 64;
|
||||
const toolCallsParseCache = new Map<string, ApiChatCompletionToolCall[]>();
|
||||
|
||||
/**
|
||||
* Safely parse the toolCalls JSON string from a DatabaseMessage.
|
||||
* Memoized: the same JSON string is re-parsed on every render during
|
||||
* streaming, which is wasted CPU since tool calls don't change mid-stream.
|
||||
*/
|
||||
function parseToolCalls(toolCallsJson?: string): ApiChatCompletionToolCall[] {
|
||||
if (!toolCallsJson) return [];
|
||||
|
||||
const cached = toolCallsParseCache.get(toolCallsJson);
|
||||
if (cached) return cached;
|
||||
|
||||
let result: ApiChatCompletionToolCall[];
|
||||
try {
|
||||
const parsed = JSON.parse(toolCallsJson);
|
||||
|
||||
return Array.isArray(parsed) ? parsed : [];
|
||||
result = Array.isArray(parsed) ? parsed : [];
|
||||
} catch {
|
||||
return [];
|
||||
result = [];
|
||||
}
|
||||
|
||||
if (toolCallsParseCache.size >= TOOL_CALLS_CACHE_MAX_SIZE) {
|
||||
toolCallsParseCache.delete(toolCallsParseCache.keys().next().value!);
|
||||
}
|
||||
toolCallsParseCache.set(toolCallsJson, result);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
|
|
@ -34,6 +34,10 @@ function escapeCode(code: string): string {
|
|||
return code.replace(AMPERSAND_REGEX, '&').replace(LT_REGEX, '<').replace(GT_REGEX, '>');
|
||||
}
|
||||
|
||||
/** Bounded cache for highlightCode results. */
|
||||
const HIGHLIGHT_CACHE_MAX_SIZE = 64;
|
||||
const highlightCache = new Map<string, string>();
|
||||
|
||||
/**
|
||||
* Highlights code using highlight.js
|
||||
* @param code - The code to highlight
|
||||
|
|
@ -47,23 +51,37 @@ function escapeCode(code: string): string {
|
|||
export function highlightCode(code: string, language: string, autoDetect = true): string {
|
||||
if (!code) return '';
|
||||
|
||||
// Cache key includes language and autoDetect flag since results differ.
|
||||
// During streaming, the same code string may be highlighted repeatedly
|
||||
// (e.g., when text after a code block changes but the code itself doesn't).
|
||||
const cacheKey = `${language}:${autoDetect}:${code}`;
|
||||
const cached = highlightCache.get(cacheKey);
|
||||
if (cached) return cached;
|
||||
|
||||
const trimmed = trimCodePadding(code);
|
||||
let result: string;
|
||||
|
||||
try {
|
||||
const lang = language.toLowerCase();
|
||||
const isSupported = hljs.getLanguage(lang);
|
||||
|
||||
if (isSupported) {
|
||||
return hljs.highlight(trimmed, { language: lang }).value;
|
||||
result = hljs.highlight(trimmed, { language: lang }).value;
|
||||
} else if (autoDetect) {
|
||||
return hljs.highlightAuto(trimmed).value;
|
||||
result = hljs.highlightAuto(trimmed).value;
|
||||
} else {
|
||||
return escapeCode(trimmed);
|
||||
result = escapeCode(trimmed);
|
||||
}
|
||||
} catch {
|
||||
// Fallback to escaped plain text
|
||||
return escapeCode(trimmed);
|
||||
result = escapeCode(trimmed);
|
||||
}
|
||||
|
||||
if (highlightCache.size >= HIGHLIGHT_CACHE_MAX_SIZE) {
|
||||
highlightCache.delete(highlightCache.keys().next().value!);
|
||||
}
|
||||
highlightCache.set(cacheKey, result);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
export { trimCodePadding };
|
||||
|
|
|
|||
|
|
@ -1,9 +1,31 @@
|
|||
import {
|
||||
CODE_BLOCK_PLACEHOLDER_REGEXP,
|
||||
CODE_BLOCK_REGEXP,
|
||||
LATEX_BACKSLASH,
|
||||
LATEX_BLOCKQUOTE_PREFIX_REGEXP,
|
||||
LATEX_CURRENCY_DOLLAR_REGEXP,
|
||||
LATEX_CURRENCY_ESCAPE,
|
||||
LATEX_DIGIT_REGEXP,
|
||||
LATEX_DISPLAY_BLOCK_REGEXP,
|
||||
LATEX_DISPLAY_CLOSE,
|
||||
LATEX_DISPLAY_CONVERT_REGEXP,
|
||||
LATEX_DISPLAY_DELIMITER,
|
||||
LATEX_DISPLAY_OPEN,
|
||||
LATEX_INLINE_CLOSE,
|
||||
LATEX_INLINE_CONVERT_REGEXP,
|
||||
LATEX_INLINE_DELIMITER,
|
||||
LATEX_INLINE_OPEN,
|
||||
LATEX_MATH_AND_CODE_PATTERN,
|
||||
LATEX_MHCHEM_CE,
|
||||
LATEX_MHCHEM_PU,
|
||||
LATEX_LINEBREAK_REGEXP,
|
||||
LATEX_NEIGHBOR_CHAR_REGEXP,
|
||||
LATEX_NON_WHITESPACE_REGEXP,
|
||||
LATEX_PLACEHOLDER_REGEXP,
|
||||
LATEX_PROTECT_REGEXP,
|
||||
LATEX_TRIGGER_REGEXP,
|
||||
MHCHEM_PATTERN_MAP
|
||||
MHCHEM_PATTERN_MAP,
|
||||
NEWLINE
|
||||
} from '$lib/constants';
|
||||
|
||||
/**
|
||||
|
|
@ -20,13 +42,13 @@ import {
|
|||
* @returns The processed string with LaTeX replaced by placeholders.
|
||||
*/
|
||||
export function maskInlineLaTeX(content: string, latexExpressions: string[]): string {
|
||||
if (!content.includes('$')) {
|
||||
if (!content.includes(LATEX_INLINE_DELIMITER)) {
|
||||
return content;
|
||||
}
|
||||
return content
|
||||
.split('\n')
|
||||
.split(NEWLINE)
|
||||
.map((line) => {
|
||||
if (line.indexOf('$') == -1) {
|
||||
if (line.indexOf(LATEX_INLINE_DELIMITER) == -1) {
|
||||
return line;
|
||||
}
|
||||
|
||||
|
|
@ -34,7 +56,7 @@ export function maskInlineLaTeX(content: string, latexExpressions: string[]): st
|
|||
let currentPosition = 0;
|
||||
|
||||
while (currentPosition < line.length) {
|
||||
const openDollarIndex = line.indexOf('$', currentPosition);
|
||||
const openDollarIndex = line.indexOf(LATEX_INLINE_DELIMITER, currentPosition);
|
||||
|
||||
if (openDollarIndex == -1) {
|
||||
processedLine += line.slice(currentPosition);
|
||||
|
|
@ -42,7 +64,7 @@ export function maskInlineLaTeX(content: string, latexExpressions: string[]): st
|
|||
}
|
||||
|
||||
// Is there a next $-sign?
|
||||
const closeDollarIndex = line.indexOf('$', openDollarIndex + 1);
|
||||
const closeDollarIndex = line.indexOf(LATEX_INLINE_DELIMITER, openDollarIndex + 1);
|
||||
|
||||
if (closeDollarIndex == -1) {
|
||||
processedLine += line.slice(currentPosition);
|
||||
|
|
@ -62,14 +84,14 @@ export function maskInlineLaTeX(content: string, latexExpressions: string[]): st
|
|||
shouldSkipAsNonLatex = true;
|
||||
}
|
||||
|
||||
if (/[A-Za-z0-9_$-]/.test(charBeforeOpen)) {
|
||||
if (LATEX_NEIGHBOR_CHAR_REGEXP.test(charBeforeOpen)) {
|
||||
// Character, digit, $, _ or - before first '$', no TeX.
|
||||
shouldSkipAsNonLatex = true;
|
||||
}
|
||||
|
||||
if (
|
||||
/[0-9]/.test(charAfterOpen) &&
|
||||
(/[A-Za-z0-9_$-]/.test(charAfterClose) || ' ' == charBeforeClose)
|
||||
LATEX_DIGIT_REGEXP.test(charAfterOpen) &&
|
||||
(LATEX_NEIGHBOR_CHAR_REGEXP.test(charAfterClose) || ' ' == charBeforeClose)
|
||||
) {
|
||||
// First $ seems to belong to an amount.
|
||||
shouldSkipAsNonLatex = true;
|
||||
|
|
@ -92,7 +114,7 @@ export function maskInlineLaTeX(content: string, latexExpressions: string[]): st
|
|||
|
||||
return processedLine;
|
||||
})
|
||||
.join('\n');
|
||||
.join(NEWLINE);
|
||||
}
|
||||
|
||||
function escapeBrackets(text: string): string {
|
||||
|
|
@ -107,9 +129,9 @@ function escapeBrackets(text: string): string {
|
|||
if (codeBlock != null) {
|
||||
return codeBlock;
|
||||
} else if (squareBracket != null) {
|
||||
return `$$${squareBracket}$$`;
|
||||
return `${LATEX_DISPLAY_DELIMITER}${squareBracket}${LATEX_DISPLAY_DELIMITER}`;
|
||||
} else if (roundBracket != null) {
|
||||
return `$${roundBracket}$`;
|
||||
return `${LATEX_INLINE_DELIMITER}${roundBracket}${LATEX_INLINE_DELIMITER}`;
|
||||
}
|
||||
|
||||
return match;
|
||||
|
|
@ -145,32 +167,49 @@ const doEscapeMhchem = false;
|
|||
* preprocessLaTeX("Price: $10. The equation is \\(x^2\\).")
|
||||
* // → "Price: $10. The equation is $x^2$."
|
||||
*/
|
||||
/** Bounded cache for preprocessLaTeX results. */
|
||||
const LATEX_CACHE_MAX_SIZE = 64;
|
||||
const latexCache = new Map<string, string>();
|
||||
|
||||
export function preprocessLaTeX(content: string): string {
|
||||
// See also:
|
||||
// https://github.com/danny-avila/LibreChat/blob/main/client/src/utils/latex.ts
|
||||
|
||||
// Memoize on the input string. During streaming the prefix before an
|
||||
// incomplete code block stays the same across multiple tokens, so the
|
||||
// full protect/restore pipeline would re-run unnecessarily.
|
||||
const cached = latexCache.get(content);
|
||||
if (cached !== undefined) return cached;
|
||||
|
||||
// Save original before the function mutates `content` through steps 0-8
|
||||
const originalContent = content;
|
||||
|
||||
// Every step below keys off a `$` or a backslash escape (\[ \] \( \) \ce{ \pu{).
|
||||
// With neither present the protect/restore passes round-trip the input
|
||||
// unchanged, so skip them: the step 2 scan is O(n^2) in line length and costs
|
||||
// ~90ms on a 26KB single-line message that contains no math at all. This
|
||||
// matters during streaming, where the whole message is reprocessed per frame.
|
||||
if (!LATEX_TRIGGER_REGEXP.test(content)) {
|
||||
if (latexCache.size >= LATEX_CACHE_MAX_SIZE) {
|
||||
latexCache.delete(latexCache.keys().next().value!);
|
||||
}
|
||||
latexCache.set(originalContent, content);
|
||||
return content;
|
||||
}
|
||||
|
||||
// Step 0: Temporarily remove blockquote markers (>) to process LaTeX correctly
|
||||
// Store the structure so we can restore it later
|
||||
const blockquoteMarkers: Map<number, string> = new Map();
|
||||
const lines = content.split('\n');
|
||||
const lines = content.split(NEWLINE);
|
||||
const processedLines = lines.map((line, index) => {
|
||||
const match = line.match(/^(>\s*)/);
|
||||
const match = line.match(LATEX_BLOCKQUOTE_PREFIX_REGEXP);
|
||||
if (match) {
|
||||
blockquoteMarkers.set(index, match[1]);
|
||||
return line.slice(match[1].length);
|
||||
}
|
||||
return line;
|
||||
});
|
||||
content = processedLines.join('\n');
|
||||
content = processedLines.join(NEWLINE);
|
||||
|
||||
// Step 1: Protect code blocks
|
||||
const codeBlocks: string[] = [];
|
||||
|
|
@ -187,58 +226,52 @@ export function preprocessLaTeX(content: string): string {
|
|||
// Match \S...\[...\] and protect them and insert a line-break.
|
||||
// Guarded: with no `\[` present this pattern still probes every start offset,
|
||||
// expanding `.*?` to the end of each line before failing - O(n^2) for nothing.
|
||||
if (content.includes('\\[')) {
|
||||
content = content.replace(
|
||||
/([\S].*?)\\\[([\s\S]*?)\\\](.*)/g,
|
||||
(match, group1, group2, group3) => {
|
||||
// Check if there are characters following the formula (display-formula in a table-cell?)
|
||||
if (group1.endsWith('\\')) {
|
||||
return match; // Backslash before \[, do nothing.
|
||||
}
|
||||
const hasSuffix = /\S/.test(group3);
|
||||
let optBreak;
|
||||
|
||||
if (hasSuffix) {
|
||||
latexExpressions.push(`\\(${group2.trim()}\\)`); // Convert into inline.
|
||||
optBreak = '';
|
||||
} else {
|
||||
latexExpressions.push(`\\[${group2}\\]`);
|
||||
optBreak = '\n';
|
||||
}
|
||||
|
||||
return `${group1}${optBreak}<<LATEX_${latexExpressions.length - 1}>>${optBreak}${group3}`;
|
||||
if (content.includes(LATEX_DISPLAY_OPEN)) {
|
||||
content = content.replace(LATEX_DISPLAY_BLOCK_REGEXP, (match, group1, group2, group3) => {
|
||||
// Check if there are characters following the formula (display-formula in a table-cell?)
|
||||
if (group1.endsWith(LATEX_BACKSLASH)) {
|
||||
return match; // Backslash before \[, do nothing.
|
||||
}
|
||||
);
|
||||
const hasSuffix = LATEX_NON_WHITESPACE_REGEXP.test(group3);
|
||||
let optBreak;
|
||||
|
||||
if (hasSuffix) {
|
||||
latexExpressions.push(`${LATEX_INLINE_OPEN}${group2.trim()}${LATEX_INLINE_CLOSE}`); // Convert into inline.
|
||||
optBreak = '';
|
||||
} else {
|
||||
latexExpressions.push(`${LATEX_DISPLAY_OPEN}${group2}${LATEX_DISPLAY_CLOSE}`);
|
||||
optBreak = NEWLINE;
|
||||
}
|
||||
|
||||
return `${group1}${optBreak}<<LATEX_${latexExpressions.length - 1}>>${optBreak}${group3}`;
|
||||
});
|
||||
}
|
||||
|
||||
// Match \(...\), \[...\], $$...$$ and protect them
|
||||
content = content.replace(
|
||||
/(\$\$[\s\S]*?\$\$|(?<!\\)\\\[[\s\S]*?\\\]|(?<!\\)\\\(.*?\\\))/g,
|
||||
(match) => {
|
||||
latexExpressions.push(match);
|
||||
content = content.replace(LATEX_PROTECT_REGEXP, (match) => {
|
||||
latexExpressions.push(match);
|
||||
|
||||
return `<<LATEX_${latexExpressions.length - 1}>>`;
|
||||
}
|
||||
);
|
||||
return `<<LATEX_${latexExpressions.length - 1}>>`;
|
||||
});
|
||||
|
||||
// Protect inline $...$ but NOT if it looks like money (e.g., $10, $3.99)
|
||||
content = maskInlineLaTeX(content, latexExpressions);
|
||||
|
||||
// Step 3: Escape standalone $ before digits (currency like $5 → \$5)
|
||||
// (Now that inline math is protected, this will only escape dollars not already protected)
|
||||
content = content.replace(/\$(?=\d)/g, '\\$');
|
||||
content = content.replace(LATEX_CURRENCY_DOLLAR_REGEXP, LATEX_CURRENCY_ESCAPE);
|
||||
|
||||
// Step 4: Restore protected LaTeX expressions (they are valid)
|
||||
content = content.replace(/<<LATEX_(\d+)>>/g, (_, index) => {
|
||||
content = content.replace(LATEX_PLACEHOLDER_REGEXP, (_, index) => {
|
||||
let expr = latexExpressions[parseInt(index)];
|
||||
const match = expr.match(LATEX_LINEBREAK_REGEXP);
|
||||
if (match) {
|
||||
// Katex: The $$-delimiters should be in their own line
|
||||
// if there are \\-line-breaks.
|
||||
const formula = match[1];
|
||||
const prefix = formula.startsWith('\n') ? '' : '\n';
|
||||
const suffix = formula.endsWith('\n') ? '' : '\n';
|
||||
expr = '$$' + prefix + formula + suffix + '$$';
|
||||
const prefix = formula.startsWith(NEWLINE) ? '' : NEWLINE;
|
||||
const suffix = formula.endsWith(NEWLINE) ? '' : NEWLINE;
|
||||
expr = LATEX_DISPLAY_DELIMITER + prefix + formula + suffix + LATEX_DISPLAY_DELIMITER;
|
||||
}
|
||||
return expr;
|
||||
});
|
||||
|
|
@ -247,7 +280,7 @@ export function preprocessLaTeX(content: string): string {
|
|||
// This must happen BEFORE restoring code blocks to avoid affecting code content
|
||||
content = escapeBrackets(content);
|
||||
|
||||
if (doEscapeMhchem && (content.includes('\\ce{') || content.includes('\\pu{'))) {
|
||||
if (doEscapeMhchem && (content.includes(LATEX_MHCHEM_CE) || content.includes(LATEX_MHCHEM_PU))) {
|
||||
content = escapeMhchem(content);
|
||||
}
|
||||
|
||||
|
|
@ -257,31 +290,38 @@ export function preprocessLaTeX(content: string): string {
|
|||
// Using the look‑behind pattern `(?<!\\)` we skip matches
|
||||
// that are preceded by a backslash, e.g.
|
||||
// `Definitions\\(also called macros)` (title of chapter 20 in The TeXbook).
|
||||
.replace(/(?<!\\)\\\((.+?)\\\)/g, '$$$1$') // inline
|
||||
.replace(LATEX_INLINE_CONVERT_REGEXP, (_, formula: string) => {
|
||||
return `${LATEX_INLINE_DELIMITER}${formula}${LATEX_INLINE_DELIMITER}`;
|
||||
}) // inline
|
||||
.replace(
|
||||
// Using the look‑behind pattern `(?<!\\)` we skip matches
|
||||
// that are preceded by a backslash, e.g. `\\[4pt]`.
|
||||
/(?<!\\)\\\[([\s\S]*?)\\\]/g, // display, see also PR #16599
|
||||
(_, content: string) => {
|
||||
return `$$${content}$$`;
|
||||
LATEX_DISPLAY_CONVERT_REGEXP, // display, see also PR #16599
|
||||
(_, formula: string) => {
|
||||
return `${LATEX_DISPLAY_DELIMITER}${formula}${LATEX_DISPLAY_DELIMITER}`;
|
||||
}
|
||||
);
|
||||
|
||||
// Step 7: Restore code blocks
|
||||
// This happens AFTER all LaTeX conversions to preserve code content
|
||||
content = content.replace(/<<CODE_BLOCK_(\d+)>>/g, (_, index) => {
|
||||
content = content.replace(CODE_BLOCK_PLACEHOLDER_REGEXP, (_, index) => {
|
||||
return codeBlocks[parseInt(index)];
|
||||
});
|
||||
|
||||
// Step 8: Restore blockquote markers
|
||||
if (blockquoteMarkers.size > 0) {
|
||||
const finalLines = content.split('\n');
|
||||
const finalLines = content.split(NEWLINE);
|
||||
const restoredLines = finalLines.map((line, index) => {
|
||||
const marker = blockquoteMarkers.get(index);
|
||||
return marker ? marker + line : line;
|
||||
});
|
||||
content = restoredLines.join('\n');
|
||||
content = restoredLines.join(NEWLINE);
|
||||
}
|
||||
|
||||
if (latexCache.size >= LATEX_CACHE_MAX_SIZE) {
|
||||
latexCache.delete(latexCache.keys().next().value!);
|
||||
}
|
||||
latexCache.set(originalContent, content);
|
||||
|
||||
return content;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -14,70 +14,94 @@ const JSON_ARRAY_CLOSE = ']';
|
|||
// comma when the model cut off mid-key.
|
||||
const TRAILING_JSON_PUNCTUATION_REGEX = /,?\s*$/;
|
||||
|
||||
/** Bounded cache for parsePartialJsonArgs results. */
|
||||
const PARTIAL_JSON_CACHE_MAX_SIZE = 32;
|
||||
const partialJsonCache = new Map<string, Record<string, unknown> | null>();
|
||||
|
||||
function cacheResult(input: string, result: Record<string, unknown> | null): void {
|
||||
if (partialJsonCache.size >= PARTIAL_JSON_CACHE_MAX_SIZE) {
|
||||
partialJsonCache.delete(partialJsonCache.keys().next().value!);
|
||||
}
|
||||
partialJsonCache.set(input, result);
|
||||
}
|
||||
|
||||
// Parse partial tool-arg JSON streamed token-by-token. Closes any
|
||||
// unterminated string and dangling open containers (in reverse order),
|
||||
// so parsers can still surface keys already received while the call
|
||||
// is still in flight.
|
||||
// is still in flight. Memoized: the char-by-char scanner runs on every
|
||||
// render during streaming even when toolArgs hasn't changed.
|
||||
export function parsePartialJsonArgs(toolArgsString: string): Record<string, unknown> | null {
|
||||
const cached = partialJsonCache.get(toolArgsString);
|
||||
if (cached !== undefined) return cached;
|
||||
|
||||
let result: Record<string, unknown> | null;
|
||||
|
||||
try {
|
||||
const parsed: unknown = JSON.parse(toolArgsString);
|
||||
if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {
|
||||
return parsed as Record<string, unknown>;
|
||||
}
|
||||
return null;
|
||||
result =
|
||||
parsed && typeof parsed === 'object' && !Array.isArray(parsed)
|
||||
? (parsed as Record<string, unknown>)
|
||||
: null;
|
||||
} catch {
|
||||
let inString = false;
|
||||
let escape = false;
|
||||
const stack: ('{' | '[')[] = [];
|
||||
result = scanPartialJson(toolArgsString);
|
||||
}
|
||||
|
||||
for (let i = 0; i < toolArgsString.length; i++) {
|
||||
const ch = toolArgsString[i];
|
||||
if (escape) {
|
||||
escape = false;
|
||||
continue;
|
||||
}
|
||||
if (ch === JSON_BACKSLASH && inString) {
|
||||
escape = true;
|
||||
continue;
|
||||
}
|
||||
if (ch === JSON_QUOTE) {
|
||||
inString = !inString;
|
||||
continue;
|
||||
}
|
||||
if (inString) continue;
|
||||
if (ch === JSON_OBJECT_OPEN) stack.push(JSON_OBJECT_OPEN);
|
||||
else if (ch === JSON_OBJECT_CLOSE) {
|
||||
if (stack.length === 0 || stack[stack.length - 1] !== JSON_OBJECT_OPEN) return null;
|
||||
stack.pop();
|
||||
} else if (ch === JSON_ARRAY_OPEN) stack.push(JSON_ARRAY_OPEN);
|
||||
else if (ch === JSON_ARRAY_CLOSE) {
|
||||
if (stack.length === 0 || stack[stack.length - 1] !== JSON_ARRAY_OPEN) return null;
|
||||
stack.pop();
|
||||
}
|
||||
}
|
||||
cacheResult(toolArgsString, result);
|
||||
return result;
|
||||
}
|
||||
|
||||
let completed = toolArgsString;
|
||||
/** Char-by-char scanner for unterminated partial JSON. */
|
||||
function scanPartialJson(toolArgsString: string): Record<string, unknown> | null {
|
||||
let inString = false;
|
||||
let escape = false;
|
||||
const stack: ('{' | '[')[] = [];
|
||||
|
||||
for (let i = 0; i < toolArgsString.length; i++) {
|
||||
const ch = toolArgsString[i];
|
||||
if (escape) {
|
||||
// Dangling escape at end of partial JSON: escape the trailing
|
||||
// backslash as a literal so we can close the string cleanly.
|
||||
completed += JSON_BACKSLASH;
|
||||
escape = false;
|
||||
continue;
|
||||
}
|
||||
if (inString) completed += JSON_QUOTE;
|
||||
if (!inString) completed = completed.replace(TRAILING_JSON_PUNCTUATION_REGEX, '');
|
||||
|
||||
// Close in reverse nesting order: innermost container first.
|
||||
for (let i = stack.length - 1; i >= 0; i--) {
|
||||
completed += stack[i] === JSON_OBJECT_OPEN ? JSON_OBJECT_CLOSE : JSON_ARRAY_CLOSE;
|
||||
if (ch === JSON_BACKSLASH && inString) {
|
||||
escape = true;
|
||||
continue;
|
||||
}
|
||||
|
||||
try {
|
||||
const parsed: unknown = JSON.parse(completed);
|
||||
if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {
|
||||
return parsed as Record<string, unknown>;
|
||||
}
|
||||
return null;
|
||||
} catch {
|
||||
return null;
|
||||
if (ch === JSON_QUOTE) {
|
||||
inString = !inString;
|
||||
continue;
|
||||
}
|
||||
if (inString) continue;
|
||||
if (ch === JSON_OBJECT_OPEN) stack.push(JSON_OBJECT_OPEN);
|
||||
else if (ch === JSON_OBJECT_CLOSE) {
|
||||
if (stack.length === 0 || stack[stack.length - 1] !== JSON_OBJECT_OPEN) return null;
|
||||
stack.pop();
|
||||
} else if (ch === JSON_ARRAY_OPEN) stack.push(JSON_ARRAY_OPEN);
|
||||
else if (ch === JSON_ARRAY_CLOSE) {
|
||||
if (stack.length === 0 || stack[stack.length - 1] !== JSON_ARRAY_OPEN) return null;
|
||||
stack.pop();
|
||||
}
|
||||
}
|
||||
|
||||
let completed = toolArgsString;
|
||||
if (escape) {
|
||||
// Dangling escape at end of partial JSON: escape the trailing
|
||||
// backslash as a literal so we can close the string cleanly.
|
||||
completed += JSON_BACKSLASH;
|
||||
}
|
||||
if (inString) completed += JSON_QUOTE;
|
||||
if (!inString) completed = completed.replace(TRAILING_JSON_PUNCTUATION_REGEX, '');
|
||||
|
||||
// Close in reverse nesting order: innermost container first.
|
||||
for (let i = stack.length - 1; i >= 0; i--) {
|
||||
completed += stack[i] === JSON_OBJECT_OPEN ? JSON_OBJECT_CLOSE : JSON_ARRAY_CLOSE;
|
||||
}
|
||||
|
||||
try {
|
||||
const parsed: unknown = JSON.parse(completed);
|
||||
return parsed && typeof parsed === 'object' && !Array.isArray(parsed)
|
||||
? (parsed as Record<string, unknown>)
|
||||
: null;
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -155,41 +155,71 @@ function parseChunk(chunk: string): SearchResult | null {
|
|||
return result;
|
||||
}
|
||||
|
||||
/** Bounded cache for extractSearchResults results. */
|
||||
const SEARCH_RESULTS_CACHE_MAX_SIZE = 32;
|
||||
const searchResultsCache = new Map<string, SearchResult[]>();
|
||||
|
||||
/**
|
||||
* Extract a SearchResult[] from a tool-result string. Returns `[]` when
|
||||
* the input does not match the expected shape — useful for branching
|
||||
* between dedicated search-results rendering and the generic tool-call
|
||||
* block.
|
||||
* block. Memoized: called per render during streaming on unchanged
|
||||
* tool result strings.
|
||||
*/
|
||||
export function extractSearchResults(text: string | undefined | null): SearchResult[] {
|
||||
if (!text) return [];
|
||||
|
||||
const cached = searchResultsCache.get(text);
|
||||
if (cached) return cached;
|
||||
|
||||
const results: SearchResult[] = [];
|
||||
for (const chunk of splitChunks(text)) {
|
||||
const parsed = parseChunk(chunk);
|
||||
if (parsed) results.push(parsed);
|
||||
}
|
||||
|
||||
if (searchResultsCache.size >= SEARCH_RESULTS_CACHE_MAX_SIZE) {
|
||||
searchResultsCache.delete(searchResultsCache.keys().next().value!);
|
||||
}
|
||||
searchResultsCache.set(text, results);
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/** Bounded cache for extractSearchQuery results. */
|
||||
const SEARCH_QUERY_CACHE_MAX_SIZE = 32;
|
||||
const searchQueryCache = new Map<string, string>();
|
||||
|
||||
/**
|
||||
* Best-effort extraction of the search query out of a tool call's JSON
|
||||
* argument blob. Currently looks for a `query` field (the convention
|
||||
* used by Exa and most web-search MCP servers); returns an empty string
|
||||
* if it cannot be located.
|
||||
* if it cannot be located. Memoized: called per render during streaming
|
||||
* on unchanged tool args strings.
|
||||
*/
|
||||
export function extractSearchQuery(toolArgs: string | undefined | null): string {
|
||||
if (!toolArgs) return '';
|
||||
|
||||
const cached = searchQueryCache.get(toolArgs);
|
||||
if (cached !== undefined) return cached;
|
||||
|
||||
let result = '';
|
||||
try {
|
||||
const parsed: unknown = JSON.parse(toolArgs);
|
||||
if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {
|
||||
const candidate = (parsed as Record<string, unknown>)[SEARCH_TOOL_QUERY_FIELD];
|
||||
if (typeof candidate === 'string') return candidate.trim();
|
||||
if (typeof candidate === 'string') result = candidate.trim();
|
||||
}
|
||||
} catch {
|
||||
return '';
|
||||
result = '';
|
||||
}
|
||||
return '';
|
||||
|
||||
if (searchQueryCache.size >= SEARCH_QUERY_CACHE_MAX_SIZE) {
|
||||
searchQueryCache.delete(searchQueryCache.keys().next().value!);
|
||||
}
|
||||
searchQueryCache.set(toolArgs, result);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
146
tools/ui/tests/unit/parse-toolcalls-memo.test.ts
Normal file
146
tools/ui/tests/unit/parse-toolcalls-memo.test.ts
Normal file
|
|
@ -0,0 +1,146 @@
|
|||
// Tests for the memoized parseToolCalls and O(1) tool message lookup in
|
||||
// deriveAgenticSections. These were added to prevent regressions where
|
||||
// streaming text tokens trigger redundant JSON.parse calls on unchanged
|
||||
// tool call data.
|
||||
|
||||
import { describe, it, expect, vi } from 'vitest';
|
||||
import { deriveAgenticSections } from '$lib/utils/agentic';
|
||||
import type { ApiChatCompletionToolCall } from '$lib/types/api';
|
||||
import type { DatabaseMessage } from '$lib/types/database';
|
||||
import { MessageRole, AgenticSectionType } from '$lib/enums';
|
||||
|
||||
function makeMessage(overrides: Partial<DatabaseMessage>): DatabaseMessage {
|
||||
return {
|
||||
id: 'm1',
|
||||
convId: 'c1',
|
||||
type: 'text',
|
||||
timestamp: 0,
|
||||
role: MessageRole.ASSISTANT,
|
||||
content: '',
|
||||
parent: null,
|
||||
children: [],
|
||||
...overrides
|
||||
} as DatabaseMessage;
|
||||
}
|
||||
|
||||
describe('parseToolCalls memoization', () => {
|
||||
it('returns the same array reference for the same JSON string', () => {
|
||||
// parseToolCalls is not exported, but deriveAgenticSections uses it
|
||||
// internally. We verify memoization through behavior: calling
|
||||
// deriveAgenticSections twice with the same toolCalls should not
|
||||
// re-parse (which we verify by checking the returned sections
|
||||
// are equivalent).
|
||||
const toolCallsJson = JSON.stringify([
|
||||
{ id: 'call_1', type: 'function', function: { name: 'test', arguments: '{}' } }
|
||||
]);
|
||||
|
||||
const msg = makeMessage({ content: 'hello', toolCalls: toolCallsJson });
|
||||
const sections1 = deriveAgenticSections(msg, [], [], false);
|
||||
const sections2 = deriveAgenticSections(msg, [], [], false);
|
||||
|
||||
expect(sections1).toHaveLength(sections2.length);
|
||||
expect(sections1[0].type).toBe(sections2[0].type);
|
||||
});
|
||||
|
||||
it('does not re-parse JSON on cache hit', () => {
|
||||
const toolCallsJson = JSON.stringify([
|
||||
{ id: 'call_1', type: 'function', function: { name: 'test', arguments: '{}' } }
|
||||
]);
|
||||
|
||||
const msg = makeMessage({ content: 'hello', toolCalls: toolCallsJson });
|
||||
const spy = vi.spyOn(JSON, 'parse');
|
||||
|
||||
deriveAgenticSections(msg, [], [], false);
|
||||
const callsAfterFirst = spy.mock.calls.length;
|
||||
|
||||
deriveAgenticSections(msg, [], [], false);
|
||||
expect(spy.mock.calls.length).toBe(callsAfterFirst);
|
||||
|
||||
spy.mockRestore();
|
||||
});
|
||||
|
||||
it('handles empty/undefined toolCalls without error', () => {
|
||||
const msg = makeMessage({ content: 'hello' });
|
||||
const sections = deriveAgenticSections(msg, [], [], false);
|
||||
|
||||
expect(sections).toHaveLength(1);
|
||||
expect(sections[0].type).toBe(AgenticSectionType.TEXT);
|
||||
});
|
||||
|
||||
it('handles invalid JSON gracefully', () => {
|
||||
const msg = makeMessage({ content: 'hello', toolCalls: '{invalid' });
|
||||
const sections = deriveAgenticSections(msg, [], [], false);
|
||||
|
||||
// Should return just the text section, no tool call sections
|
||||
expect(sections).toHaveLength(1);
|
||||
expect(sections[0].type).toBe(AgenticSectionType.TEXT);
|
||||
});
|
||||
});
|
||||
|
||||
describe('deriveAgenticSections O(1) tool message lookup', () => {
|
||||
it('matches tool messages to tool calls by toolCallId', () => {
|
||||
const toolCallsJson = JSON.stringify([
|
||||
{ id: 'call_1', type: 'function', function: { name: 'test_1', arguments: '{}' } },
|
||||
{ id: 'call_2', type: 'function', function: { name: 'test_2', arguments: '{}' } }
|
||||
]);
|
||||
|
||||
const toolMessages = [
|
||||
makeMessage({ role: MessageRole.TOOL, toolCallId: 'call_1', content: 'result_1' }),
|
||||
makeMessage({ role: MessageRole.TOOL, toolCallId: 'call_2', content: 'result_2' })
|
||||
];
|
||||
|
||||
const msg = makeMessage({ content: 'hello', toolCalls: toolCallsJson });
|
||||
const sections = deriveAgenticSections(msg, toolMessages, [], false);
|
||||
|
||||
// Expect: TEXT + 2 TOOL_CALL sections
|
||||
const toolCallSections = sections.filter((s) => s.type === AgenticSectionType.TOOL_CALL);
|
||||
expect(toolCallSections).toHaveLength(2);
|
||||
expect(toolCallSections[0].toolResult).toBe('result_1');
|
||||
expect(toolCallSections[1].toolResult).toBe('result_2');
|
||||
});
|
||||
|
||||
it('handles missing tool messages (pending calls during streaming)', () => {
|
||||
const toolCallsJson = JSON.stringify([
|
||||
{ id: 'call_1', type: 'function', function: { name: 'test', arguments: '{}' } }
|
||||
]);
|
||||
|
||||
const msg = makeMessage({ content: '', toolCalls: toolCallsJson });
|
||||
const sections = deriveAgenticSections(msg, [], [], true);
|
||||
|
||||
const toolCallSection = sections.find((s) => s.type === AgenticSectionType.TOOL_CALL_PENDING);
|
||||
expect(toolCallSection).toBeDefined();
|
||||
expect(toolCallSection?.content).toBe('');
|
||||
});
|
||||
|
||||
it('scales with many tool calls (no O(n^2) blowup)', () => {
|
||||
const N = 100;
|
||||
const toolCalls = Array.from(
|
||||
{ length: N },
|
||||
(_, i): ApiChatCompletionToolCall => ({
|
||||
id: `call_${i}`,
|
||||
type: 'function',
|
||||
function: { name: `tool_${i}`, arguments: '{}' }
|
||||
})
|
||||
);
|
||||
const toolCallsJson = JSON.stringify(toolCalls);
|
||||
|
||||
const toolMessages = Array.from({ length: N }, (_, i) =>
|
||||
makeMessage({
|
||||
role: MessageRole.TOOL,
|
||||
toolCallId: `call_${i}`,
|
||||
content: `result_${i}`
|
||||
})
|
||||
);
|
||||
|
||||
const msg = makeMessage({ content: 'hello', toolCalls: toolCallsJson });
|
||||
|
||||
// If the lookup were still O(n^2), this would be noticeably slow
|
||||
const start = Date.now();
|
||||
const sections = deriveAgenticSections(msg, toolMessages, [], false);
|
||||
const elapsed = Date.now() - start;
|
||||
|
||||
const toolCallSections = sections.filter((s) => s.type === AgenticSectionType.TOOL_CALL);
|
||||
expect(toolCallSections).toHaveLength(N);
|
||||
expect(elapsed).toBeLessThan(100); // Should be fast with O(1) lookup
|
||||
});
|
||||
});
|
||||
2
vendor/cpp-httplib/CMakeLists.txt
vendored
2
vendor/cpp-httplib/CMakeLists.txt
vendored
|
|
@ -41,7 +41,7 @@ if (LLAMA_BUILD_BORINGSSL)
|
|||
set(FIPS OFF CACHE BOOL "Enable FIPS (BoringSSL)")
|
||||
|
||||
set(BORINGSSL_GIT "https://boringssl.googlesource.com/boringssl" CACHE STRING "BoringSSL git repository")
|
||||
set(BORINGSSL_VERSION "0.20260713.0" CACHE STRING "BoringSSL version")
|
||||
set(BORINGSSL_VERSION "0.20260728.0" CACHE STRING "BoringSSL version")
|
||||
|
||||
message(STATUS "Fetching BoringSSL version ${BORINGSSL_VERSION}")
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue