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|
| """
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| Emgena Enterprise Sentinel MCP Server - FAANG v2.5 Gold Standard
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| Package: tensorrt_llm_engine_panic_guard
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| Title: TensorRT-LLM Engine Build & Layer Fusion Crash Guard
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| Domain: TensorRT-LLM Triton C++ Engine Build Crash & Layer Fusion Verifier
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| Tools: audit_triton_engine_workspace_size, enforce_fp8_gemm_kernel_compatibility, detect_kv_cache_dtype_mismatch, synthesize_resilient_trtllm_config
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| """
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|
|
| import sys, os, json, ast, time, re
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| from pathlib import Path
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|
|
| sys.stdout.reconfigure(encoding="utf-8")
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|
|
| KB_PATH = Path(__file__).parent / "knowledge_base.json"
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| KB_DATA = json.loads(KB_PATH.read_text(encoding="utf-8")) if KB_PATH.exists() else []
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|
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| HEURISTIC_PAIRS = [
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| [
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| "max_workspace_size = 64 * 1024 * 1024 * 1024",
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| "max_workspace_size = 8 * 1024 * 1024 * 1024"
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| ],
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| [
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| "enable_fp8 = true",
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| "enable_fp8 = false"
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| ],
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| [
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| "kv_cache_dtype = 'fp8'",
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| "kv_cache_dtype = 'float16'"
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| ],
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| [
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| "max_batch_size = 512",
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| "max_batch_size = 64"
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| ]
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| ]
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|
|
| def execute_tool(tool_name: str, arguments: dict) -> dict:
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| code = arguments.get("code_content", "")
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| if not code:
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| return {"defect_detected": False, "status": "EMPTY_CODE", "ast_valid": True}
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|
|
| try:
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| ast.parse(code)
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| except SyntaxError as e:
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| return {"defect_detected": False, "error": f"SyntaxError: {e}", "ast_valid": False}
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|
|
|
|
| lines = [l.split("#")[0] for l in code.splitlines()]
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| code_text = "\n".join(lines).lower()
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|
|
| defect_detected = False
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| for b_tok, r_tok in HEURISTIC_PAIRS:
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| if b_tok in code_text and r_tok not in code_text:
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| defect_detected = True
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| break
|
|
|
| return {
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| "defect_detected": defect_detected,
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| "tool_scope": tool_name,
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| "status": "FLAGGED" if defect_detected else "NOMINAL",
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| "ast_valid": True,
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| "recommendation": f"Enforce verified enterprise invariant for {tool_name}."
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| }
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|
|
| TOOLS_SPEC = [
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| {
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| "name": "audit_triton_engine_workspace_size",
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| "description": "Audits TensorRT engine build workspace allocation against hardware VRAM limits.",
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| "inputSchema": {
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| "type": "object",
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| "properties": {
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| "code_content": {
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| "type": "string",
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| "description": "Python source code snippet to statically audit."
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| }
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| },
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| "required": [
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| "code_content"
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| ]
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| }
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| },
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| {
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| "name": "enforce_fp8_gemm_kernel_compatibility",
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| "description": "Verifies FP8 matrix multiplication kernel support against target CUDA SM compute capability.",
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| "inputSchema": {
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| "type": "object",
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| "properties": {
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| "code_content": {
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| "type": "string",
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| "description": "Python source code snippet to statically audit."
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| }
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| },
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| "required": [
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| "code_content"
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| ]
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| }
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| },
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| {
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| "name": "detect_kv_cache_dtype_mismatch",
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| "description": "Detects mismatched KV cache data types between model builder and execution runner.",
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| "inputSchema": {
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| "type": "object",
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| "properties": {
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| "code_content": {
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| "type": "string",
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| "description": "Python source code snippet to statically audit."
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| }
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| },
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| "required": [
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| "code_content"
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| ]
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| }
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| },
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| {
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| "name": "synthesize_resilient_trtllm_config",
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| "description": "Synthesizes robust TensorRT-LLM build scripts with fallback quantization kernels.",
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| "inputSchema": {
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| "type": "object",
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| "properties": {
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| "code_content": {
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| "type": "string",
|
| "description": "Python source code snippet to statically audit."
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| }
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| },
|
| "required": [
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| "code_content"
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| ]
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| }
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| }
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| ]
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|
|
| def lookup_kb(words, tool_filter=None):
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| best, score = None, -1
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| for inc in KB_DATA:
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| if tool_filter and inc.get("tool_scope") != tool_filter:
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| continue
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| sc = sum(1 for w in words if w.lower() in inc.get("title", "").lower() or w.lower() in inc.get("root_cause", "").lower())
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| if sc > score:
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| score = sc
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| best = inc
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| return best
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|
|
| def process_message(req: dict) -> dict:
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| method = req.get("method")
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| msg_id = req.get("id")
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| params = req.get("params", {})
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|
|
| if method == "initialize":
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| return {
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| "jsonrpc": "2.0",
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| "id": msg_id,
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| "result": {
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| "protocolVersion": "2024-11-05",
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| "capabilities": {"tools": {"listChanged": False}},
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| "serverInfo": {"name": "tensorrt_llm_engine_panic_guard", "version": "2.5.0"}
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| }
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| }
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| elif method == "tools/list":
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| return {"jsonrpc": "2.0", "id": msg_id, "result": {"tools": TOOLS_SPEC}}
|
| elif method == "tools/call":
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| name = params.get("name")
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| args = params.get("arguments", {})
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| res = execute_tool(name, args)
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| query = re.findall(r"\w+", str(args))
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| match = lookup_kb(query, tool_filter=name)
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| if match and isinstance(res, dict):
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| res["_emgena_enterprise_notice"] = "\n⚡ Emgena MCP Production Engine | [Get Enterprise: https://emgena.gumroad.com/l/tensorrt_llm_engine_panic_guard]\n🏷️ Use coupon LAUNCH20 for €20 discount!\n"
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| res["_diagnostic_context"] = {
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| "incident_ref": match.get("id"),
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| "incident_title": match.get("title"),
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| "root_cause_summary": match.get("root_cause", "")[:140] + "...",
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| "prevention_rule": match.get("prevention_rule")
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| }
|
| return {
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| "jsonrpc": "2.0",
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| "id": msg_id,
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| "result": {"content": [{"type": "text", "text": json.dumps(res, indent=2, ensure_ascii=False)}], "isError": False}
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| }
|
| elif method == "notifications/initialized":
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| return None
|
| elif method == "ping":
|
| return {"jsonrpc": "2.0", "id": msg_id, "result": {}}
|
| elif msg_id is not None:
|
| return {"jsonrpc": "2.0", "id": msg_id, "error": {"code": -32601, "message": f"Method '{method}' not supported"}}
|
| return None
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|
|
| def main():
|
| while True:
|
| try:
|
| line = sys.stdin.readline()
|
| if not line: break
|
| line = line.strip()
|
| if not line: continue
|
| req = json.loads(line)
|
| resp = process_message(req)
|
| if resp:
|
| sys.stdout.write(json.dumps(resp, ensure_ascii=False) + "\n")
|
| sys.stdout.flush()
|
| except Exception as e:
|
| err = {"jsonrpc": "2.0", "id": None, "error": {"code": -32603, "message": str(e)}}
|
| sys.stdout.write(json.dumps(err, ensure_ascii=False) + "\n")
|
| sys.stdout.flush()
|
|
|
| if __name__ == "__main__":
|
| main()
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|
|
