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chat : split specialized parsers into common/parsers (#27764)

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* 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)
2026-09-08 09:29:37 +03:00
committed by GitHub
parent 7d701b5929
commit 895c045fd1
19 changed files with 2534 additions and 2411 deletions
Download Patch File Download Diff File Expand all files Collapse all files
common/parsers/lfm2.cpp
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#include "parsers.h"
// LFM2 format detection: template uses <|tool_list_start|>[...]<|tool_list_end|> around the tool list
// and <|tool_call_start|>[...]<|tool_call_end|> around each tool call
bool is_lfm2_template(const std::string & src) {
return src.find("<|tool_list_start|>") != std::string::npos &&
src.find("<|tool_list_end|>") != std::string::npos;
}
// LFM2/LFM2.5 parser. Tool calls are almost Python-style and parallel-capable
// (except dotted names and JSON literals true/false/null).
// Always wrapped in <|tool_call_start|>[name(args)]<|tool_call_end|> with optional <think> reasoning.
// tool_list_tokens preserves LFM2 system tool-list markers.
common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl,
const autoparser::generation_params & inputs,
bool tool_list_tokens) {
common_chat_params data;
const std::string TOOL_CALL_START = "<|tool_call_start|>";
const std::string TOOL_CALL_END = "<|tool_call_end|>";
const std::string TOOL_LIST_START = "<|tool_list_start|>";
const std::string TOOL_LIST_END = "<|tool_list_end|>";
const std::string THINK_START = "<think>";
const std::string THINK_END = "</think>";
const std::string GEN_PROMPT = "<|im_start|>assistant\n";
// Copy reasoning to the "thinking" field the template expects
auto adjusted_messages = json::array();
for (auto msg : inputs.messages) {
if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
msg["thinking"] = msg.at("reasoning_content");
}
adjusted_messages.push_back(msg);
}
data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.supports_thinking = true;
data.preserved_tokens = { TOOL_CALL_START, TOOL_CALL_END, THINK_START, THINK_END };
if (tool_list_tokens) {
data.preserved_tokens.push_back(TOOL_LIST_START);
data.preserved_tokens.push_back(TOOL_LIST_END);
}
data.thinking_start_tag = THINK_START;
data.thinking_end_tags = {THINK_END};
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
// Gate by reasoning format and whether the template supports <think>
auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE &&
tmpl.source().find(THINK_START) != std::string::npos;
auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
if (inputs.has_continuation()) {
const auto & msg = inputs.continue_msg;
data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
data.generation_prompt += THINK_END + msg.render_content();
}
data.prompt += data.generation_prompt;
}
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
auto generation_prompt = p.literal(GEN_PROMPT);
auto end = p.end();
auto reasoning = p.eps();
if (extract_reasoning) {
reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
}
if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
if (has_response_format) {
auto response_format = p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema));
return generation_prompt + reasoning + response_format + end;
}
return generation_prompt + reasoning + p.content(p.rest()) + end;
}
auto tool_calls = p.rule("tool-calls",
p.trigger_rule("tool-call",
p.literal(TOOL_CALL_START) +
p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls, /* allow_json_literals = */ true) +
p.literal(TOOL_CALL_END)
)
);
auto content = p.content(p.until(TOOL_CALL_START));
return generation_prompt + reasoning + content + 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.at("parameters");
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, TOOL_CALL_START }
};
}
return data;
}