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https://github.com/ggml-org/llama.cpp.git
synced 2026-06-15 10:16:45 +02:00
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5 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| e725a1a982 | |||
| 3db4da56a5 | |||
| 476aa3fd57 | |||
| 0d8831543c | |||
| 65c797c4fa |
@@ -55,7 +55,15 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
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bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
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std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
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std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
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std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
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std::string arguments = "";
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if (tool_call.contains("arguments")) {
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if (tool_call.at("arguments").is_object()) {
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arguments = tool_call.at("arguments").dump();
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} else {
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arguments = tool_call.at("arguments");
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}
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}
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return add_tool_call(name, id, arguments);
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}
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+129
@@ -606,6 +606,7 @@ const char * common_chat_format_name(common_chat_format format) {
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case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
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case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
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case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
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case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
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case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
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default:
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throw std::runtime_error("Unknown chat format");
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@@ -618,6 +619,7 @@ const char * common_reasoning_format_name(common_reasoning_format format) {
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case COMMON_REASONING_FORMAT_AUTO: return "auto";
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case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
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case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
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case COMMON_REASONING_FORMAT_GRANITE: return "granite";
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default:
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throw std::runtime_error("Unknown reasoning format");
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}
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@@ -1734,6 +1736,124 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
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builder.add_content(builder.consume_rest());
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}
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static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
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common_chat_params data;
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// Pass thinking context for Granite template
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json additional_context = {
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{"thinking", inputs.enable_thinking},
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};
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data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
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data.format = COMMON_CHAT_FORMAT_GRANITE;
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if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
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if (!inputs.enable_thinking) {
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data.prompt += "</think>";
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} else {
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data.thinking_forced_open = true;
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}
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}
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if (!inputs.tools.is_null()) {
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// Granite uses <|tool_call|> followed by JSON list
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data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
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data.grammar = build_grammar([&](const common_grammar_builder & builder) {
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std::vector<std::string> tool_rules;
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foreach_function(inputs.tools, [&](const json & tool) {
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const auto & function = tool.at("function");
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std::string name = function.at("name");
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auto parameters = function.at("parameters");
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builder.resolve_refs(parameters);
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tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
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"-args", {
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{"type", "object"},
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{"properties", {
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{"name", {{"const", name}}},
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{"arguments", parameters},
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}},
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{"required", json::array({"name", "arguments"})},
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})));
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});
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auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
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auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
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if (data.thinking_forced_open) {
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builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
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} else {
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builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
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}
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data.grammar_triggers.push_back({
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COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
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"<|tool_call|>"
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});
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data.preserved_tokens = {
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"<think>",
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"</think>",
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"<response>",
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"</response>",
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"<|tool_call|>",
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};
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});
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} else {
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// Handle thinking tags for non-tool responses
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if (data.thinking_forced_open && inputs.enable_thinking) {
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data.grammar_lazy = false;
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data.grammar = build_grammar([&](const common_grammar_builder & builder) {
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builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
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});
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data.preserved_tokens = {
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"<think>",
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"</think>",
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"<response>",
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"</response>",
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};
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}
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}
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return data;
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}
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static void common_chat_parse_granite(common_chat_msg_parser & builder) {
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// Parse thinking tags
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builder.try_parse_reasoning("<think>", "</think>");
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// Parse response tags using regex
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static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
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if (auto res = builder.try_find_regex(response_regex)) {
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// Extract the content between the tags (capture group 1)
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auto content = builder.str(res->groups[1]);
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builder.add_content(content);
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builder.move_to(res->groups[0].end);
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}
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if (!builder.syntax().parse_tool_calls) {
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builder.add_content(builder.consume_rest());
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return;
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}
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// Look for tool calls
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static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
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if (auto res = builder.try_find_regex(tool_call_regex)) {
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builder.move_to(res->groups[0].end);
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// Expect JSON array of tool calls
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auto tool_calls_data = builder.consume_json();
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if (tool_calls_data.json.is_array()) {
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if (!builder.add_tool_calls(tool_calls_data.json)) {
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builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
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}
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} else {
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builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
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}
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} else {
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builder.add_content(builder.consume_rest());
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}
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}
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static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
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common_chat_params data;
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data.prompt = apply(tmpl, inputs);
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@@ -1805,6 +1925,11 @@ static common_chat_params common_chat_templates_apply_jinja(
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return common_chat_params_init_command_r7b(tmpl, params);
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}
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// Granite (IBM) - detects thinking / tools support
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if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) {
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return common_chat_params_init_granite(tmpl, params);
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}
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// Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
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if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
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return common_chat_params_init_hermes_2_pro(tmpl, params);
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@@ -1865,6 +1990,7 @@ static common_chat_params common_chat_templates_apply_legacy(
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int alloc_size = 0;
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std::vector<llama_chat_message> chat;
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std::vector<std::string> contents;
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for (const auto & msg : inputs.messages) {
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auto content = msg.content;
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for (const auto & part : msg.content_parts) {
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@@ -1966,6 +2092,9 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
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case COMMON_CHAT_FORMAT_COMMAND_R7B:
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common_chat_parse_command_r7b(builder);
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break;
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case COMMON_CHAT_FORMAT_GRANITE:
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common_chat_parse_granite(builder);
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break;
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case COMMON_CHAT_FORMAT_GPT_OSS:
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common_chat_parse_gpt_oss(builder);
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break;
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@@ -109,6 +109,7 @@ enum common_chat_format {
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COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
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COMMON_CHAT_FORMAT_HERMES_2_PRO,
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COMMON_CHAT_FORMAT_COMMAND_R7B,
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COMMON_CHAT_FORMAT_GRANITE,
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COMMON_CHAT_FORMAT_GPT_OSS,
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COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
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@@ -239,6 +239,7 @@ enum common_reasoning_format {
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COMMON_REASONING_FORMAT_AUTO,
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COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
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COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
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COMMON_REASONING_FORMAT_GRANITE, // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
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};
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struct common_params {
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@@ -1071,6 +1071,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
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}
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}
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}
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// if the node is still unassigned, assign it to the first backend that supports it
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for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
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ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
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}
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GGML_ASSERT(*cur_backend_id != -1);
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}
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// pass 5: split graph, find tensors that need to be copied
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@@ -1098,7 +1103,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
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const int node_backend_id = tensor_backend_id(node);
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assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
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GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
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// check if we should start a new split based on the sources of the current node
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bool need_new_split = false;
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@@ -1156,7 +1161,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
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size_t src_id = hash_id(src);
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const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
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assert(src_backend_id != -1); // all inputs should be assigned by now
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GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now
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if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
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if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
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@@ -35,7 +35,7 @@
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// ggml-backend interface
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std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
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std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
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static std::vector<ggml_backend_buffer_type_t> bufts = []() {
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std::vector<ggml_backend_buffer_type_t> bufts;
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@@ -57,8 +57,6 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
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}
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#endif
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bufts.push_back(NULL);
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return bufts;
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}();
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@@ -66,14 +64,20 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
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}
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static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
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return ggml_backend_cpu_get_extra_buffers_type().data();
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static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
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std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
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bufts.push_back(nullptr);
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return bufts;
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}();
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return extra_bufts.data();
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GGML_UNUSED(device);
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}
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static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
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for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
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if (extra && extra == buft) {
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for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
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if (extra == buft) {
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return true;
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}
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}
|
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@@ -397,20 +401,13 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
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return true;
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}
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// extra_buffer_op?
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for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
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if (extra) {
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auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
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if (buf_extra && buf_extra->supports_op(dev, op)) {
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return true;
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}
|
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}
|
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}
|
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|
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// the other case need host buffer.
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for (int i = 0; i < GGML_MAX_SRC; i++) {
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if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
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return false;
|
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// check extra buffer types
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// note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
|
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for (int i = 0; i < 4; i++) {
|
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if (op->src[i] && op->src[i]->buffer &&
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ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
|
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auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
|
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return buf_extra->supports_op(dev, op);
|
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}
|
||||
}
|
||||
|
||||
|
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@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
|
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} // namespace ggml::cpu
|
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|
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bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
|
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for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
|
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for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
|
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if (extra && extra->context) {
|
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auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context;
|
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auto tensor_traits = buf_extra->get_tensor_traits(op);
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@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
|
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}
|
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|
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bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
|
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for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
|
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for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
|
||||
if (extra && extra->context) {
|
||||
auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context;
|
||||
auto tensor_traits = buf_extra->get_tensor_traits(op);
|
||||
|
||||
@@ -33,6 +33,6 @@ class extra_buffer_type {
|
||||
} // namespace ggml::cpu
|
||||
|
||||
// implemented in ggml-cpu.cpp.
|
||||
std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
|
||||
std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();
|
||||
|
||||
#endif
|
||||
|
||||
@@ -55,6 +55,7 @@ endfunction()
|
||||
|
||||
set(GGML_OPENCL_KERNELS
|
||||
add
|
||||
add_id
|
||||
argsort
|
||||
clamp
|
||||
cpy
|
||||
|
||||
@@ -345,6 +345,7 @@ struct ggml_backend_opencl_context {
|
||||
cl_command_queue queue;
|
||||
|
||||
cl_program program_add;
|
||||
cl_program program_add_id;
|
||||
cl_program program_clamp;
|
||||
cl_program program_cpy;
|
||||
cl_program program_cvt;
|
||||
@@ -404,6 +405,7 @@ struct ggml_backend_opencl_context {
|
||||
cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16;
|
||||
cl_kernel kernel_div, kernel_div_row, kernel_div_f16, kernel_div_row_f16;
|
||||
cl_kernel kernel_sub, kernel_sub_row, kernel_sub_f16, kernel_sub_row_f16;
|
||||
cl_kernel kernel_add_id;
|
||||
cl_kernel kernel_scale;
|
||||
cl_kernel kernel_silu, kernel_silu_4;
|
||||
cl_kernel kernel_gelu, kernel_gelu_4;
|
||||
@@ -412,7 +414,7 @@ struct ggml_backend_opencl_context {
|
||||
cl_kernel kernel_relu;
|
||||
cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
|
||||
cl_kernel kernel_clamp;
|
||||
cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_geglu_erf, kernel_geglu_quick,
|
||||
cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_swiglu_oai, kernel_geglu_erf, kernel_geglu_quick,
|
||||
kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
|
||||
cl_kernel kernel_norm;
|
||||
cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
|
||||
@@ -681,6 +683,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
|
||||
GGML_LOG_CONT(".");
|
||||
}
|
||||
|
||||
// add_id
|
||||
{
|
||||
#ifdef GGML_OPENCL_EMBED_KERNELS
|
||||
const std::string kernel_src {
|
||||
#include "add_id.cl.h"
|
||||
};
|
||||
#else
|
||||
const std::string kernel_src = read_file("add_id.cl");
|
||||
#endif
|
||||
backend_ctx->program_add_id =
|
||||
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
|
||||
|
||||
CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err));
|
||||
GGML_LOG_CONT(".");
|
||||
}
|
||||
|
||||
// clamp
|
||||
{
|
||||
#ifdef GGML_OPENCL_EMBED_KERNELS
|
||||
@@ -787,6 +805,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
|
||||
CL_CHECK((backend_ctx->kernel_geglu = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_reglu = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_swiglu = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_swiglu_oai = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu_oai", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_geglu_erf = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_erf", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_geglu_quick = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_quick", &err), err));
|
||||
CL_CHECK((backend_ctx->kernel_geglu_f16 = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_f16", &err), err));
|
||||
@@ -2467,6 +2486,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
|
||||
return (op->src[0]->type == op->src[1]->type) &&
|
||||
(op->src[0]->type == op->type) &&
|
||||
(op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
|
||||
case GGML_OP_ADD_ID:
|
||||
return op->src[0]->type == GGML_TYPE_F32;
|
||||
case GGML_OP_UNARY:
|
||||
switch (ggml_get_unary_op(op)) {
|
||||
case GGML_UNARY_OP_GELU:
|
||||
@@ -2488,6 +2509,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
|
||||
case GGML_GLU_OP_GEGLU:
|
||||
case GGML_GLU_OP_REGLU:
|
||||
case GGML_GLU_OP_SWIGLU:
|
||||
case GGML_GLU_OP_SWIGLU_OAI:
|
||||
case GGML_GLU_OP_GEGLU_ERF:
|
||||
case GGML_GLU_OP_GEGLU_QUICK:
|
||||
return ggml_is_contiguous_1(op->src[0]) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
|
||||
@@ -3824,6 +3846,75 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_cl_add_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
GGML_ASSERT(src0);
|
||||
GGML_ASSERT(src0->extra);
|
||||
GGML_ASSERT(src1);
|
||||
GGML_ASSERT(src1->extra);
|
||||
GGML_ASSERT(dst);
|
||||
GGML_ASSERT(dst->extra);
|
||||
|
||||
const ggml_tensor * src2 = dst->src[2];
|
||||
GGML_ASSERT(src2);
|
||||
GGML_ASSERT(src2->extra);
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(src2->type == GGML_TYPE_I32);
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
||||
|
||||
GGML_ASSERT(ggml_is_contiguous_rows(src0));
|
||||
|
||||
const int ne00 = src0->ne[0];
|
||||
const int ne01 = src0->ne[1];
|
||||
const int ne02 = src0->ne[2];
|
||||
|
||||
const cl_ulong nb01 = src0->nb[1];
|
||||
const cl_ulong nb02 = src0->nb[2];
|
||||
|
||||
const cl_ulong nb11 = src1->nb[1];
|
||||
|
||||
const cl_ulong nb21 = src2->nb[1];
|
||||
|
||||
const int ne0 = dst->ne[0];
|
||||
const int ne1 = dst->ne[1];
|
||||
|
||||
ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
|
||||
|
||||
ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
|
||||
ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
|
||||
ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
|
||||
ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
|
||||
|
||||
cl_ulong offset0 = extra0->offset + src0->view_offs;
|
||||
cl_ulong offset1 = extra1->offset + src1->view_offs;
|
||||
cl_ulong offset2 = extra2->offset + src2->view_offs;
|
||||
cl_ulong offsetd = extrad->offset + dst->view_offs;
|
||||
|
||||
cl_kernel kernel = backend_ctx->kernel_add_id;
|
||||
|
||||
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
|
||||
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
|
||||
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
|
||||
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
|
||||
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra2->data_device));
|
||||
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset2));
|
||||
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
|
||||
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
|
||||
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01));
|
||||
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02));
|
||||
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb11));
|
||||
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb21));
|
||||
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne0));
|
||||
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne1));
|
||||
|
||||
int nth = MIN(ne00, (int) backend_ctx->get_kernel_workgroup_size(kernel));
|
||||
size_t global_work_size[] = { (size_t)ne01*nth, (size_t)ne02, 1 };
|
||||
size_t local_work_size[] = { (size_t)nth, 1, 1 };
|
||||
|
||||
backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
|
||||
}
|
||||
|
||||
static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
GGML_ASSERT(src0);
|
||||
GGML_ASSERT(src0->extra);
|
||||
@@ -7005,6 +7096,9 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
|
||||
kernel = backend_ctx->kernel_swiglu_f16;
|
||||
}
|
||||
break;
|
||||
case GGML_GLU_OP_SWIGLU_OAI:
|
||||
kernel = backend_ctx->kernel_swiglu_oai;
|
||||
break;
|
||||
case GGML_GLU_OP_GEGLU_ERF:
|
||||
if (dst->type == GGML_TYPE_F32) {
|
||||
kernel = backend_ctx->kernel_geglu_erf;
|
||||
@@ -7040,7 +7134,10 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
|
||||
|
||||
const cl_ulong nb1 = dst->nb[1];
|
||||
|
||||
const int swp = ((const int32_t *) dst->op_params)[1];
|
||||
const int swp = ggml_get_op_params_i32(dst, 1);
|
||||
const float alpha = ggml_get_op_params_f32(dst, 2);
|
||||
const float limit = ggml_get_op_params_f32(dst, 3);
|
||||
|
||||
const int ne00_off = src1 ? 0 : (swp ? ne0 : 0);
|
||||
const int ne10_off = src1 ? 0 : (swp ? 0 : ne0);
|
||||
|
||||
@@ -7057,6 +7154,11 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
|
||||
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int), &ne00_off));
|
||||
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne10_off));
|
||||
|
||||
if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU_OAI) {
|
||||
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float), &limit));
|
||||
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float), &alpha));
|
||||
}
|
||||
|
||||
const size_t nrows = ggml_nrows(src0);
|
||||
size_t nth = 512;
|
||||
size_t global_work_size[] = {nrows*nth, 1, 1};
|
||||
@@ -7113,6 +7215,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
|
||||
}
|
||||
func = ggml_cl_add;
|
||||
break;
|
||||
case GGML_OP_ADD_ID:
|
||||
if (!any_on_device) {
|
||||
return false;
|
||||
}
|
||||
func = ggml_cl_add_id;
|
||||
break;
|
||||
case GGML_OP_MUL:
|
||||
if (!any_on_device) {
|
||||
return false;
|
||||
|
||||
@@ -0,0 +1,42 @@
|
||||
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
|
||||
|
||||
//------------------------------------------------------------------------------
|
||||
// add_id
|
||||
//------------------------------------------------------------------------------
|
||||
kernel void kernel_add_id(
|
||||
global char * src0,
|
||||
ulong offset0,
|
||||
global char * src1,
|
||||
ulong offset1,
|
||||
global char * src2,
|
||||
ulong offset2,
|
||||
global char * dst,
|
||||
ulong offsetd,
|
||||
ulong nb01,
|
||||
ulong nb02,
|
||||
ulong nb11,
|
||||
ulong nb21,
|
||||
int ne0,
|
||||
int ne1
|
||||
) {
|
||||
src0 = (global char*)((global char*)src0 + offset0);
|
||||
src1 = (global char*)((global char*)src1 + offset1);
|
||||
src2 = (global char*)((global char*)src2 + offset2);
|
||||
dst = (global char*)((global char*)dst + offsetd);
|
||||
|
||||
int i1 = get_group_id(0);
|
||||
int i2 = get_group_id(1);
|
||||
|
||||
const int i11 = *((global const int *) (src2 + i1*sizeof(int) + i2*nb21));
|
||||
|
||||
const size_t nb1 = ne0 * sizeof(float);
|
||||
const size_t nb2 = ne1 * nb1;
|
||||
|
||||
global float * dst_row = (global float *)((global char *)dst + i1*nb1 + i2*nb2);
|
||||
global float * src0_row = (global float *)((global char *)src0 + i1*nb01 + i2*nb02);
|
||||
global float * src1_row = (global float *)((global char *)src1 + i11*nb11);
|
||||
|
||||
for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
|
||||
dst_row[i0] = src0_row[i0] + src1_row[i0];
|
||||
}
|
||||
}
|
||||
@@ -202,6 +202,47 @@ kernel void kernel_swiglu_f16(
|
||||
}
|
||||
}
|
||||
|
||||
//------------------------------------------------------------------------------
|
||||
// swiglu_oai
|
||||
//------------------------------------------------------------------------------
|
||||
kernel void kernel_swiglu_oai(
|
||||
global char * src0,
|
||||
ulong offset0,
|
||||
global char * src1,
|
||||
ulong offset1,
|
||||
global char * dst,
|
||||
ulong offsetd,
|
||||
ulong nb01,
|
||||
ulong nb11,
|
||||
int ne0,
|
||||
ulong nb1,
|
||||
int ne00_off,
|
||||
int ne10_off,
|
||||
float limit,
|
||||
float alpha
|
||||
) {
|
||||
src0 = (global char*)((global char*)src0 + offset0);
|
||||
src1 = (global char*)((global char*)src1 + offset1);
|
||||
dst = (global char*)((global char*)dst + offsetd);
|
||||
|
||||
global float * src0_row = (global float *) ((global char *) src0 + get_group_id(0)*nb01) + ne00_off;
|
||||
global float * src1_row = (global float *) ((global char *) src1 + get_group_id(0)*nb11) + ne10_off;
|
||||
global float * dst_row = (global float *) ((global char *) dst + get_group_id(0)*nb1);
|
||||
|
||||
for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
|
||||
float x0 = src0_row[i0];
|
||||
float x1 = src1_row[i0];
|
||||
|
||||
x0 = min(x0, limit);
|
||||
x1 = max(min(x1, limit), -limit);
|
||||
|
||||
float out_glu = x0 / (1.0f + exp(-x0 * alpha));
|
||||
out_glu = out_glu * (1.0f + x1);
|
||||
|
||||
dst_row[i0] = out_glu;
|
||||
}
|
||||
}
|
||||
|
||||
//------------------------------------------------------------------------------
|
||||
// geglu_erf
|
||||
//------------------------------------------------------------------------------
|
||||
|
||||
@@ -0,0 +1,59 @@
|
||||
{# Alias tools -> available_tools #}
|
||||
{%- if tools and not available_tools -%}
|
||||
{%- set available_tools = tools -%}
|
||||
{%- endif -%}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{%- set system_message = messages[0]['content'] %}
|
||||
{%- set loop_messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{%- set system_message = "Knowledge Cutoff Date: April 2024. Today's Date: " + strftime_now('%B %d, %Y') + ". You are Granite, developed by IBM." %}
|
||||
{%- if available_tools and documents %}
|
||||
{%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
|
||||
{%- elif available_tools %}
|
||||
{%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
|
||||
{%- elif documents %}
|
||||
{%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
|
||||
{%- elif thinking %}
|
||||
{%- set system_message = system_message + " You are a helpful AI assistant.
|
||||
Respond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between <think></think> and write your response between <response></response> for each user query." %}
|
||||
{%- else %}
|
||||
{%- set system_message = system_message + " You are a helpful AI assistant." %}
|
||||
{%- endif %}
|
||||
{%- if 'citations' in controls and documents %}
|
||||
{%- set system_message = system_message + '
|
||||
Use the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
|
||||
{%- endif %}
|
||||
{%- if 'hallucinations' in controls and documents %}
|
||||
{%- set system_message = system_message + '
|
||||
Finally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}
|
||||
{%- endif %}
|
||||
{%- set loop_messages = messages %}
|
||||
{%- endif %}
|
||||
{{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>
|
||||
' }}
|
||||
{%- if available_tools %}
|
||||
{{- '<|start_of_role|>available_tools<|end_of_role|>' }}
|
||||
{{- available_tools | tojson(indent=4) }}
|
||||
{{- '<|end_of_text|>
|
||||
' }}
|
||||
{%- endif %}
|
||||
{%- if documents %}
|
||||
{%- for document in documents %}
|
||||
{{- '<|start_of_role|>document {"document_id": "' + document['doc_id'] | string + '"}<|end_of_role|>
|
||||
' }}
|
||||
{{- document['text'] }}
|
||||
{{- '<|end_of_text|>
|
||||
' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- for message in loop_messages %}
|
||||
{{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>
|
||||
' }}
|
||||
{%- if loop.last and add_generation_prompt %}
|
||||
{{- '<|start_of_role|>assistant' }}
|
||||
{%- if controls %}
|
||||
{{- ' ' + controls | tojson()}}
|
||||
{%- endif %}
|
||||
{{- '<|end_of_role|>' }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
@@ -625,8 +625,6 @@ int32_t llm_chat_apply_template(
|
||||
} else if (tmpl == LLM_CHAT_TEMPLATE_YANDEX) {
|
||||
// Yandex template ("\n\n" is defined as EOT token)
|
||||
|
||||
ss << "<s>";
|
||||
|
||||
for (size_t i = 0; i < chat.size(); i++) {
|
||||
std::string role(chat[i]->role);
|
||||
if (role == "user") {
|
||||
|
||||
@@ -277,9 +277,9 @@ int main(void) {
|
||||
{
|
||||
/* .name= */ "yandex/YandexGPT-5-Lite-8B-instruct",
|
||||
/* .template_str= */ "<s>{%- set names = {'assistant': ' Ассистент:', 'user': ' Пользователь:'} %}\n{%- set tools_prefix = 'Тебе доступны следующие функции:' %}\n{%- macro __render_tool(tool) %}\n {%- set name = tool.function.name %}\n {%- set description = tool.function.description|default('') %}\n {%- set parameters = tool.function.parameters|tojson %}\n {{- '\\n' }}function {{ '{' }}'name':'{{ name }}',\n {%- if tool.function.description %}'description':'{{ description }}',{% endif %}\n'parameters':{{ parameters }}\n {{- '}' }}\n{%- endmacro %}\n{%- macro __render_tools(tools) %}\n {{- tools_prefix }}\n {%- for tool in tools %}\n {{- __render_tool(tool) }}\n {%- endfor %}\n {{- '\\n\\n' }}\n{%- endmacro %}\n{%- macro __render_tool_message(message) %}\n {{- '\\n\\nРезультат вызова' }} {{ message.name }}: {{ message.content }} {{ '\\n\\n' }}\n{%- endmacro %}\n{%- if tools -%}\n {{- __render_tools(tools) }}\n{%- endif -%}\n{%- macro __render_user_message(message) %}\n{{ names.user }} {{ message.content + '\\n\\n' }}\n{%- endmacro %}\n{%- macro __render_assistant_message(message) %}\n {{- names.assistant }}\n {%- set call = message['function_call'] %}\n {%- if call %}\n {{- '\\n[TOOL_CALL_START]' }}{{ call.name }}{{ '\\n' }}{{ call.arguments|tojson }}\n {%- else %}\n {{- ' ' + message.content + '\\n\\n' }}\n {%- endif %}\n{%- endmacro %}\n{%- if not add_generation_prompt is defined %}\n{%- set add_generation_prompt = false %}\n{%- endif %}\n{%- for message in messages %}\n {%- if message['role'] == 'user' %}\n {{- __render_user_message(message) }}\n {%- endif %}\n {%- if message.role == 'assistant' and not loop.last %}\n {{- __render_assistant_message(message) }}\n {%- endif %}\n {%- if message.role == 'tool' %}\n {{- __render_tool_message(message) }}\n {%- endif %}\n {%- if loop.last %}\n {{- ' Ассистент:[SEP]' }}\n {%- endif %}\n{%- endfor %}\n",
|
||||
/* .expected_output= */ "<s> Пользователь: Hello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент: I am an assistant \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
|
||||
/* .expected_output= */ " Пользователь: Hello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент: I am an assistant \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
|
||||
/* .expected_output_jinja= */ "<s> Пользователь: You are a helpful assistant\nHello\n\n Ассистент: Hi there\n\n Пользователь: Who are you\n\n Ассистент: I am an assistant \n\n Пользователь: Another question\n\n Ассистент:[SEP]",
|
||||
/* .bos_token= */ "",
|
||||
/* .bos_token= */ "<s>",
|
||||
/* .eos_token= */ "",
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1386,6 +1386,59 @@ static void test_template_output_parsers() {
|
||||
"{\"arg1\": 1}\n"
|
||||
"```<|tool▁call▁end|><|tool▁calls▁end|>");
|
||||
}
|
||||
{
|
||||
auto tmpls = read_templates("models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja");
|
||||
std::vector<std::string> end_tokens{ "<|end_of_text|>" };
|
||||
|
||||
assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
|
||||
|
||||
assert_equals(COMMON_CHAT_FORMAT_GRANITE, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
|
||||
|
||||
// Test parsing regular content
|
||||
assert_msg_equals(message_assist,
|
||||
common_chat_parse(
|
||||
"Hello, world!\nWhat's up?",
|
||||
/* is_partial= */ false,
|
||||
{COMMON_CHAT_FORMAT_GRANITE}));
|
||||
|
||||
// Test parsing content with thinking
|
||||
assert_msg_equals(message_assist_thoughts,
|
||||
common_chat_parse(
|
||||
"<think>I'm\nthinking</think>Hello, world!\nWhat's up?",
|
||||
/* is_partial= */ false,
|
||||
{
|
||||
/* .format = */ COMMON_CHAT_FORMAT_GRANITE,
|
||||
/* .reasoning_format = */ COMMON_REASONING_FORMAT_GRANITE,
|
||||
}));
|
||||
|
||||
// Test parsing tool calls
|
||||
assert_msg_equals(message_assist_call,
|
||||
common_chat_parse(
|
||||
"<|tool_call|>[{\"name\": \"special_function\", \"arguments\": {\"arg1\": 1}}]",
|
||||
/* is_partial= */ false,
|
||||
{COMMON_CHAT_FORMAT_GRANITE}));
|
||||
|
||||
// Test template generation for regular content
|
||||
test_templates(tmpls.get(), end_tokens, message_assist, tools,
|
||||
"Hello, world!\nWhat's up?",
|
||||
/* expect_grammar_triggered= */ false);
|
||||
|
||||
// Test template generation for tool calls
|
||||
test_templates(tmpls.get(), end_tokens, message_assist_call_id, tools,
|
||||
"{\n"
|
||||
" \"tool_calls\": [\n"
|
||||
" {\n"
|
||||
" \"name\": \"special_function\",\n"
|
||||
" \"arguments\": {\n"
|
||||
" \"arg1\": 1\n"
|
||||
" },\n"
|
||||
" \"id\": \"123456789\"\n"
|
||||
" }\n"
|
||||
" ]\n"
|
||||
"}",
|
||||
/* expect_grammar_triggered= */ false
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
static void test_msg_diffs_compute() {
|
||||
|
||||
@@ -374,7 +374,7 @@ static void print_usage(int /* argc */, char ** argv) {
|
||||
printf(" -embd, --embeddings <0|1> (default: %s)\n",
|
||||
join(cmd_params_defaults.embeddings, ",").c_str());
|
||||
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
|
||||
printf(" -ot --override-tensors <tensor name pattern>=<buffer type>;...\n");
|
||||
printf(" -ot --override-tensor <tensor name pattern>=<buffer type>;...\n");
|
||||
printf(" (default: disabled)\n");
|
||||
printf(" -nopo, --no-op-offload <0|1> (default: 0)\n");
|
||||
printf("\n");
|
||||
|
||||
Reference in New Issue
Block a user