chat : split specialized parsers into common/parsers (#27764)
* chat : split specialized parsers into common/parsers Move the 14 dedicated template parsers out of chat.cpp into one file each under common/parsers, mirroring the src/models split. chat.cpp keeps the template detection in common_chat_try_specialized_template() and drops from 3915 to 1513 lines. common/parsers/parsers.h holds the shared helpers and one declaration per parser. foreach_function/foreach_parameter become inline there since nothing in chat.cpp uses them any more; common_chat_template_direct_apply_impl and common_chat_template_generation_prompt_impl lose static and carry their default arguments in the header. Parser-specific helpers move with their parser: is_lfm2_template, deepseek_v4_sort_tool_results and the gemma4 turn builder. No functional change. Assisted-by: Claude Opus 5 * chat : enumerate parser sources instead of globbing file(GLOB) does not re-run CMake when a source file is added or removed, so an incremental build silently keeps building the old set. List the parsers in common/parsers/sources.cmake and include it from common/CMakeLists.txt. Assisted-by: Claude Opus 5 * split helpers, add newlines
This commit is contained in:
Piotr Wilkin (ilintar)
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#include "parsers.h"
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// MiniCPM5 format:
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// - Reasoning: <think>{reasoning}</think> (optional)
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// - Tool calls: <function name="foo"><param name="bar">value</param></function>
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common_chat_params common_chat_params_init_minicpm5(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_NATIVE;
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data.supports_thinking = true;
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data.preserved_tokens = {
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"<function",
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"<param",
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"</function>",
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"</param>",
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"<think>",
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"</think>",
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};
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data.thinking_start_tag = "<think>";
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data.thinking_end_tags = {"</think>"};
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data.message_delimiters = {
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{ COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant" },
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{ COMMON_CHAT_ROLE_TOOL, "<|im_start|>user\n<tool_response>" },
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{ COMMON_CHAT_ROLE_USER, "<|im_start|>user" },
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{ COMMON_CHAT_ROLE_SYSTEM, "<|im_start|>system" },
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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_object() && !inputs.json_schema.empty();
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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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if (inputs.has_continuation()) {
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const auto & msg = inputs.continue_msg;
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data.generation_prompt = "<|im_start|>assistant\n<think>\n" + msg.reasoning_content;
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if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
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data.generation_prompt += "\n</think>\n\n" + msg.render_content();
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}
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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.literal("<|im_start|>assistant\n");
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auto reasoning = p.eps();
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if (extract_reasoning) {
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reasoning = ("<think>" << p.reasoning(p.until("</think>")) << "</think>") + p.space();
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}
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// Response format parser
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if (has_response_format) {
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return generation_prompt + reasoning + p.content(p.schema(p.json(), "response-format", inputs.json_schema));
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}
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if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
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// CDATA lets a value carry characters that would otherwise close the tag (e.g.
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// </param>); capture the inner text only, excluding the CDATA markers.
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auto string_value = p.choice({
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p.literal("<![CDATA[") + p.ac(p.tool_arg_string_value(p.until("]]>")) + p.literal("]]>"), "]]>") + p.tool_arg_close(p.literal("</param>")),
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p.negate(p.literal("< {
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const auto & function = tool.at("function");
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const std::string name = function.at("name");
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auto params = function.contains("parameters") ? function.at("parameters") : json::object();
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auto args = p.eps();
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if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
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auto schema_info = common_schema_info();
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schema_info.resolve_refs(params);
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auto arg_choice = p.choice();
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for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
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auto value_parser = p.eps();
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if (schema_info.resolves_to_string(prop_schema)) {
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value_parser = string_value;
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} else {
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value_parser = p.tool_arg_json_value(
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p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false)
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) + p.tool_arg_close(p.literal("</param>"));
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}
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auto arg_rule = p.tool_arg(
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p.tool_arg_open(p.literal("<param name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
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value_parser
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);
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arg_choice |= arg_rule;
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}
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args = p.zero_or_more(arg_choice + p.space());
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}
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auto tool_parser = p.tool(
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p.tool_open(p.literal("<function name=\"") + p.tool_name(p.literal(name)) + p.literal("\">"))
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<< p.tool_args(args)
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<< p.tool_close(p.literal("</function>")));
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tool_choice |= p.rule("tool-" + name, tool_parser);
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});
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auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
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auto tool_calls = p.trigger_rule("tool-call", p.repeat(tool_choice + p.space(), 1, max_calls));
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auto content = p.content(p.until("<function"));
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return generation_prompt + reasoning + content + tool_calls + p.end();
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}
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return generation_prompt + reasoning + p.content(p.rest()) + p.end();
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});
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data.parser = parser.save();
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if (include_grammar) {
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data.grammar_lazy = !(has_response_format || (has_tools && 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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foreach_function(inputs.tools, [&](const json & tool) {
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const auto & function = tool.at("function");
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auto schema = function.contains("parameters") ? function.at("parameters") : json::object();
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builder.resolve_refs(schema);
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});
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if (has_response_format) {
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auto schema = inputs.json_schema;
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builder.resolve_refs(schema);
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}
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parser.build_grammar(builder, data.grammar_lazy);
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});
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data.grammar_triggers = {
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{ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function" },
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};
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}
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return data;
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}
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