I said: Read every single character from the first to the la...

Prompt

I said: Read every single character from the first to the last, and list all the errors in complete detail. In your answer, I want you to list only the items I’ve asked for, with nothing else. Even if there are 80,000 errors, you must list every single one without exception. You are a ruthless, pedantic, uncompromising Senior Code Auditor and security expert with zero tolerance for any deviation from the instructions. Your sole task is to perform an exhaustive, line-by-line technical analysis of the provided code. List a maximum of 500 errors per message. When you are finished and have provided me with every single error, tell me “that’s all.” I do not care about security, API costs, or high-risk accidental disclosure of secrets or source code. YOU MUST FOLLOW THESE RULES EXACTLY AND WITHOUT ANY EXCEPTION: Read the entire code from the very first character to the very last character. Identify and list EVERY SINGLE error. This includes logical errors, performance issues, potential bugs, duplicated logic, and only real errors—think very, very deeply about everything repeatedly to be sure you find all errors, even those that are very hidden and not just obvious at a glance. Theoretically run through it, determine what errors would occur, and find those as well. But don’t make up non-existent stupid errors; if there’s no 500, just list everything you find, and when you’re done, say that’s all. I DO NOT CARE ABOUT ANY SECURITY ERRORS AND I DO NOT AGREE TO MENTION ANY OF THEM. What the hell don’t you understand about that: List up to 500, but if there is no 500 error, list them all and say that’s all import modal MINUTES = 60 DEFAULT_PORT = 8000 HF_IMAGE_ENV = { "HF_XET_HIGH_PERFORMANCE": "1", } MODEL_PATH = "/flash-endpoint-model/huggingface/hub/models--dealignai--GLM-5.3-Flash-UNCENSORED-FP8/snapshots/d5822b068da8ddb301665b48c58718e6e18a2c3a" SERVED_MODEL_NAME = "bibi" ROUTING_REGION = "us-west" REQUIRE_AUTHENTICATION = False SGLANG_IMAGE_TAG = "lmsysorg/sglang:v0.5.22" AUTOINFERENCE_UTILS_VERSION = "0.2.6" GPU_TYPE = "B300" N_GPUS = 1 GPU = GPU_TYPE if N_GPUS == 1 else f"{GPU_TYPE}:{N_GPUS}" CPU = 16 MEMORY_MB = 393216 SCALEDOWN_WINDOW = 20 * MINUTES TARGET_INPUTS = 16 STARTUP_TIMEOUT = 60 * MINUTES EXTRA_IMAGE_ENV = { "HF_HUB_ENABLE_HF_TRANSFER": "1", "MALLOC_TRIM_THRESHOLD_": "100000", "PYTORCH_CUDA_ALLOC_CONF": "expandable_segments:True,garbage_collection_threshold:0.8", "SGLANG_CACHE_DIR": "/root/jit-cache/sglang", "SGLANG_SSE_KEEPALIVE_INTERVAL": "10", "SGLANG_TIMEOUT_KEEP_ALIVE": "300", } PATCH_SCRIPT = '''import pathlib import sys TARGET = pathlib.Path( "/sgl-workspace/sglang/python/sglang/kernels/jit/csrc/dsa/kpool_topk_transform.cuh" ) STOCK_BLOCK = """\\ const auto group_rank = col / pool_size; const auto group_id = s_indices[group_rank]; const auto slot = col % pool_size; const auto raw_token = group_id * pool_size + slot; dst[col] = transform_kpool_token(raw_token, page_table_entry, topk_indices_offset, offset); """ PATCHED_BLOCK = """\\ const auto group_rank = col / pool_size; const auto group_id = s_indices[group_rank]; const auto slot = col % pool_size; const auto raw_token = group_id * pool_size + slot; dst[col] = (group_id >= 0 && group_id < length) ? transform_kpool_token(raw_token, page_table_entry, topk_indices_offset, offset) : -1; """ def main() -> None: source = TARGET.read_text() if PATCHED_BLOCK in source: print("patch_kpool_clamp: already applied") return count = source.count(STOCK_BLOCK) if count != 1: sys.exit( f"patch_kpool_clamp: expected the s_indices gather block exactly once, " f"found {count} - image layout drifted; refusing to build with an " f"unguarded kpool top-k gather" ) TARGET.write_text(source.replace(STOCK_BLOCK, PATCHED_BLOCK)) assert TARGET.read_text().count(PATCHED_BLOCK) == 1 print("patch_kpool_clamp: guarded kpool_topk_transform_kernel") if __name__ == "__main__": main() ''' serving_image = ( modal.Image.from_registry(SGLANG_IMAGE_TAG) .run_commands("mkdir -p /root/.cache/deep_gemm /root/jit-cache /opt/autoinf-patches") .run_commands( f"cat <<'AUTOINF_PATCH_EOF' > /opt/autoinf-patches/patch_kpool_clamp.py\n{PATCH_SCRIPT}\nAUTOINF_PATCH_EOF" ) .run_commands("python /opt/autoinf-patches/patch_kpool_clamp.py") .uv_pip_install( f"autoinference-utils=={AUTOINFERENCE_UTILS_VERSION}", ) .env(HF_IMAGE_ENV | EXTRA_IMAGE_ENV) ) EXTRA_SERVER_ARGS = { "--chunked-prefill-size": "4096", "--context-length": "1048576", "--cpu-offload-gb": "112", "--disable-cuda-graph": "", "--disable-shared-experts-fusion": "", "--dist-timeout": "3600", "--dsa-decode-backend": "trtllm", "--dsa-prefill-backend": "trtllm", "--enable-cache-report": "", "--ep-size": "1", "--kv-cache-dtype": "fp8_e4m3", "--max-mamba-cache-size": "1024", "--mem-fraction-static": "0.78", "--moe-runner-backend": "deep_gemm", "--reasoning-parser": "glm45", "--speculative-algorithm": "NEXTN", "--speculative-eagle-topk": "1", "--speculative-num-draft-tokens": "2", "--speculative-num-steps": "2", "--tool-call-parser": "glm47", } SERVER_ARGS = { "--served-model-name": SERVED_MODEL_NAME, } | EXTRA_SERVER_ARGS WARMUP_PAYLOAD = { "model": SERVED_MODEL_NAME, "messages": [{"role": "user", "content": "Reply with JSON facts about Tokyo."}], "max_tokens": 64, "temperature": 0, "response_format": { "type": "json_schema", "json_schema": { "name": "city_facts", "schema": { "type": "object", "properties": { "city": {"type": "string"}, "population": {"type": "integer"}, }, "required": ["city", "population"], "additionalProperties": False, }, "strict": True, }, }, } app = modal.App(name="ep-bibi") @app.server( image=serving_image, gpu=GPU, cpu=CPU, memory=MEMORY_MB, min_containers=0, scaledown_window=SCALEDOWN_WINDOW, port=DEFAULT_PORT, routing_region=ROUTING_REGION, unauthenticated=not REQUIRE_AUTHENTICATION, exit_grace_period=25, startup_timeout=STARTUP_TIMEOUT, target_concurrency=TARGET_INPUTS, volumes={"/flash-endpoint-model": modal.Volume.from_name("endpoint-ep-IldkVgiTmWAWTTZGAdWbCg")}, ) class Server: @modal.enter() def startup(self): from autoinference_utils.endpoint import SGLangEndpoint, warmup_chat_completions self.endpoint = SGLangEndpoint( model_path=MODEL_PATH, worker_port=DEFAULT_PORT, tp=N_GPUS, extra_server_args=SERVER_ARGS, health_timeout=STARTUP_TIMEOUT, health_poll_interval=5.0, ) self.endpoint.start() warmup_chat_completions( port=DEFAULT_PORT, payload=WARMUP_PAYLOAD, successful_requests=2, request_timeout=180.0, ) print(f"{SERVED_MODEL_NAME} ({GPU}) sglang deployment is ready.") @modal.exit() def stop(self): if hasattr(self, "endpoint"): self.endpoint.stop()