[tab] /fast/project/HFMI_SynergyUnit/mihir/logs/train/qwen3-0.6b-grpo-fsdp/run_qwen3-0.6b-grpo-fsdp_36287.log (log)
#!/usr/bin/env bash

#SBATCH --nodes=1
#SBATCH --ntasks-per-node=4
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:2
#SBATCH --mem=600GB
#SBATCH --output=/fast/project/HFMI_SynergyUnit/mihir/logs/train/qwen3-0.6b-grpo-fsdp/%x_%j.log
#SBATCH --error=/fast/project/HFMI_SynergyUnit/mihir/logs/train/qwen3-0.6b-grpo-fsdp/%x_%j.log
#SBATCH --job-name=run_qwen3-0.6b-grpo-fsdp

cat $0

source ~/.bashrc

micromamba activate miles

sh scripts/run-qwen3-0.6B-fsdp.shRunning inside SLURM, Job ID 36287.
2025-12-11 19:40:13,362	INFO scripts.py:1371 -- Did not find any active Ray processes.
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pkill: killing pid 2714858 failed: Operation not permitted
pkill: killing pid 2714860 failed: Operation not permitted
pkill: killing pid 2714862 failed: Operation not permitted
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HAS_NVLINK: 0 (detected 0 NVLink references)
Detected Head Node IP: 10.86.2.40
2025-12-11 19:40:19,936	INFO usage_lib.py:447 -- Usage stats collection is disabled.
2025-12-11 19:40:19,938	INFO scripts.py:919 -- Local node IP: 10.86.2.40
2025-12-11 19:40:31,141	SUCC scripts.py:963 -- --------------------
2025-12-11 19:40:31,142	SUCC scripts.py:964 -- Ray runtime started.
2025-12-11 19:40:31,143	SUCC scripts.py:965 -- --------------------
2025-12-11 19:40:31,144	INFO scripts.py:967 -- Next steps
2025-12-11 19:40:31,145	INFO scripts.py:970 -- To add another node to this Ray cluster, run
2025-12-11 19:40:31,146	INFO scripts.py:973 --   ray start --address='10.86.2.40:6379'
2025-12-11 19:40:31,146	INFO scripts.py:982 -- To connect to this Ray cluster:
2025-12-11 19:40:31,147	INFO scripts.py:984 -- import ray
2025-12-11 19:40:31,148	INFO scripts.py:985 -- ray.init(_node_ip_address='10.86.2.40')
2025-12-11 19:40:31,149	INFO scripts.py:997 -- To submit a Ray job using the Ray Jobs CLI:
2025-12-11 19:40:31,150	INFO scripts.py:998 --   RAY_API_SERVER_ADDRESS='http://10.86.2.40:8265' ray job submit --working-dir . -- python my_script.py
2025-12-11 19:40:31,151	INFO scripts.py:1007 -- See https://docs.ray.io/en/latest/cluster/running-applications/job-submission/index.html 
2025-12-11 19:40:31,151	INFO scripts.py:1011 -- for more information on submitting Ray jobs to the Ray cluster.
2025-12-11 19:40:31,152	INFO scripts.py:1016 -- To terminate the Ray runtime, run
2025-12-11 19:40:31,153	INFO scripts.py:1017 --   ray stop
2025-12-11 19:40:31,154	INFO scripts.py:1020 -- To view the status of the cluster, use
2025-12-11 19:40:31,155	INFO scripts.py:1021 --   ray status
2025-12-11 19:40:31,155	INFO scripts.py:1025 -- To monitor and debug Ray, view the dashboard at 
2025-12-11 19:40:31,156	INFO scripts.py:1026 --   10.86.2.40:8265
2025-12-11 19:40:31,157	INFO scripts.py:1033 -- If connection to the dashboard fails, check your firewall settings and network configuration.
2025-12-11 19:40:33,692	INFO cli.py:41 -- Job submission server address: http://10.86.2.40:8265
2025-12-11 19:40:45,178	SUCC cli.py:65 -- -------------------------------------------------------
2025-12-11 19:40:45,180	SUCC cli.py:66 -- Job 'raysubmit_Vg4BLu1rdFx1T4Qv' submitted successfully
2025-12-11 19:40:45,181	SUCC cli.py:67 -- -------------------------------------------------------
2025-12-11 19:40:45,182	INFO cli.py:291 -- Next steps
2025-12-11 19:40:45,183	INFO cli.py:292 -- Query the logs of the job:
2025-12-11 19:40:45,185	INFO cli.py:294 -- ray job logs raysubmit_Vg4BLu1rdFx1T4Qv
2025-12-11 19:40:45,186	INFO cli.py:296 -- Query the status of the job:
2025-12-11 19:40:45,187	INFO cli.py:298 -- ray job status raysubmit_Vg4BLu1rdFx1T4Qv
2025-12-11 19:40:45,189	INFO cli.py:300 -- Request the job to be stopped:
2025-12-11 19:40:45,190	INFO cli.py:302 -- ray job stop raysubmit_Vg4BLu1rdFx1T4Qv
2025-12-11 19:40:45,194	INFO cli.py:312 -- Tailing logs until the job exits (disable with --no-wait):
2025-12-11 19:40:44,867	INFO job_manager.py:568 -- Runtime env is setting up.
Running entrypoint for job raysubmit_Vg4BLu1rdFx1T4Qv: python3 train.py --hf-checkpoint /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B --load /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B --ref-load /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B --prompt-data /fast/project/HFMI_SynergyUnit/tab_model/huggingface/dapo-math-17k/dapo-math-17k.jsonl --input-key prompt --label-key label --apply-chat-template --rollout-shuffle --balance-data --rm-type deepscaler --num-rollout 100 --rollout-batch-size 8 --n-samples-per-prompt 8 --rollout-max-response-len 4096 --rollout-temperature 0.8 --global-batch-size 64 --optimizer adam --lr 1e-6 --lr-decay-style constant --weight-decay 0.1 --adam-beta1 0.9 --adam-beta2 0.98 --use-kl-loss --advantage-estimator grpo --kl-loss-coef 0.00 --kl-loss-type low_var_kl --kl-coef 0.00 --entropy-coef 0.00 --eps-clip 0.2 --eps-clip-high 0.28 --use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen3-0.6b-grpo-fsdp --rollout-num-gpus-per-engine 2 --sglang-mem-fraction-static 0.5 --sglang-decode-log-interval 1000 --sglang-chunked-prefill-size 4096 --sglang-attention-backend fa3 --train-backend fsdp --update-weight-buffer-size 536870912 --gradient-checkpointing --attn-implementation flash_attention_3 --train-env-vars {\"PYTORCH_CUDA_ALLOC_CONF\":\"expandable_segments:True\"} --use-dynamic-batch-size --max-tokens-per-gpu 9216 --actor-num-nodes 1 --actor-num-gpus-per-node 2 --colocate --use-fault-tolerance --dump-details /fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/qwen3-600M-fsdp-1116-noref/dump_details
/fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
[2025-12-11 19:41:01] INFO trace.py:52: opentelemetry package is not installed, tracing disabled
DEBUG: Configured logger.
2025-12-11 19:41:04,883	INFO worker.py:1696 -- Using address 10.86.2.40:6379 set in the environment variable RAY_ADDRESS
2025-12-11 19:41:04,893	INFO worker.py:1837 -- Connecting to existing Ray cluster at address: 10.86.2.40:6379...
2025-12-11 19:41:04,914	INFO worker.py:2014 -- Connected to Ray cluster. View the dashboard at http://10.86.2.40:8265 
/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/worker.py:2062: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0
  warnings.warn(
DEBUG: Created placement groups.
wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: WARNING The `shared` mode feature is experimental and may change. Please contact support@wandb.com for guidance and to report any issues.
wandb: setting up run zvddkqo0
wandb: Tracking run with wandb version 0.23.1
wandb: Run data is saved locally in /fast/home/mihir.mahajan/Projects/miles/wandb/run-20251211_194109-zvddkqo0
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run qwen3-0.6b-grpo-fsdp_8btmedqp-RANK_0
wandb: ⭐️ View project at https://wandb.ai/instant-uv/crowd-pilot-miles
wandb: 🚀 View run at https://wandb.ai/instant-uv/crowd-pilot-miles/runs/zvddkqo0
wandb: Detected [openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
DEBUG: Initialized tracking.
(pid=3066141) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(pid=3066141)   import pynvml  # type: ignore[import]
(RolloutManager pid=3066141) DEBUG: configure_logger() executed
(RolloutManager pid=3066141) DEBUG: self.args = args executed
(RolloutManager pid=3066141) DEBUG: self.pg = pg executed
(RolloutManager pid=3066141) 2025-12-11 18:41:23  WARN sgl_model_gateway::mcp::manager: /home/runner/work/sglang/sglang/src/mcp/manager.rs:90: No static MCP servers connected
(RolloutManager pid=3066141) [2025-12-11 19:41:23] trace.py:52 - opentelemetry package is not installed, tracing disabled
(RolloutManager pid=3066141) [2025-12-11 19:41:23] rollout.py:587 - Launch router with args: RouterArgs(worker_urls=[], host='10.86.2.40', port=3848, mini_lb=False, pd_disaggregation=False, prefill_urls=[], decode_urls=[], policy='cache_aware', prefill_policy=None, decode_policy=None, worker_startup_timeout_secs=1800, worker_startup_check_interval=30, cache_threshold=0.3, balance_abs_threshold=64, balance_rel_threshold=1.5, eviction_interval_secs=120, max_tree_size=67108864, max_payload_size=536870912, bucket_adjust_interval_secs=5, dp_aware=False, enable_igw=False, api_key=None, log_dir=None, log_level='warn', service_discovery=False, selector={}, service_discovery_port=80, service_discovery_namespace=None, prefill_selector={}, decode_selector={}, bootstrap_port_annotation='sglang.ai/bootstrap-port', prometheus_port=5360, prometheus_host='0.0.0.0', request_id_headers=None, request_timeout_secs=14400, max_concurrent_requests=-1, queue_size=100, queue_timeout_secs=60, rate_limit_tokens_per_second=None, cors_allowed_origins=[], retry_max_retries=5, retry_initial_backoff_ms=50, retry_max_backoff_ms=30000, retry_backoff_multiplier=1.5, retry_jitter_factor=0.2, disable_retries=False, health_failure_threshold=3, health_success_threshold=2, health_check_timeout_secs=5, health_check_interval_secs=60, health_check_endpoint='/health', cb_failure_threshold=10, cb_success_threshold=3, cb_timeout_duration_secs=60, cb_window_duration_secs=120, disable_circuit_breaker=False, model_path=None, tokenizer_path=None, chat_template=None, tokenizer_cache_enable_l0=False, tokenizer_cache_l0_max_entries=10000, tokenizer_cache_enable_l1=False, tokenizer_cache_l1_max_memory=52428800, reasoning_parser=None, tool_call_parser=None, mcp_config_path=None, backend='sglang', history_backend='memory', oracle_wallet_path=None, oracle_tns_alias=None, oracle_connect_descriptor=None, oracle_username=None, oracle_password=None, oracle_pool_min=1, oracle_pool_max=16, oracle_pool_timeout_secs=30, postgres_db_url=None, postgres_pool_max=16, client_cert_path=None, client_key_path=None, ca_cert_paths=[], enable_trace=False, otlp_traces_endpoint='localhost:4317')
(RolloutManager pid=3066141) DEBUG: _start_router(args) executed
(RolloutManager pid=3066141) [2025-12-11 19:41:26] rollout.py:598 - Router launched at 10.86.2.40:3848
(RolloutManager pid=3066141) wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
(RolloutManager pid=3066141) wandb: WARNING The `shared` mode feature is experimental and may change. Please contact support@wandb.com for guidance and to report any issues.
(RolloutManager pid=3066141) wandb: WARNING Using a boolean value for 'reinit' is deprecated. Use 'return_previous' or 'finish_previous' instead.
(RolloutManager pid=3066141) wandb: setting up run zvddkqo0
(RolloutManager pid=3066141) wandb: Tracking run with wandb version 0.23.1
(RolloutManager pid=3066141) wandb: Run data is saved locally in /fast/home/mihir.mahajan/Projects/miles/wandb/run-20251211_194126-zvddkqo0
(RolloutManager pid=3066141) wandb: Run `wandb offline` to turn off syncing.
(RolloutManager pid=3066141) wandb: Resuming run qwen3-0.6b-grpo-fsdp_8btmedqp-RANK_0
(RolloutManager pid=3066141) wandb: ⭐️ View project at https://wandb.ai/instant-uv/crowd-pilot-miles
(RolloutManager pid=3066141) wandb: 🚀 View run at https://wandb.ai/instant-uv/crowd-pilot-miles/runs/zvddkqo0
(RolloutManager pid=3066141) DEBUG: init_tracking executed
(RolloutManager pid=3066141) DEBUG: init_http_client(args) executed
(RolloutManager pid=3066141) wandb: Detected [openai] in use.
(RolloutManager pid=3066141) wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
(RolloutManager pid=3066141) wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
(RolloutManager pid=3066141) DEBUG: data_source_cls loaded
(RolloutManager pid=3066141) DEBUG: self.data_source initialized
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:21: SyntaxWarning: invalid escape sequence '\{'
(RolloutManager pid=3066141)   m = re.search("^\\\	ext\{(?P<text>.+?)\}$", answer)
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:127: SyntaxWarning: invalid escape sequence '\%'
(RolloutManager pid=3066141)   string = string.replace("\%", "")
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:164: SyntaxWarning: invalid escape sequence '\^'
(RolloutManager pid=3066141)   BAD_REGEXES = ["\^[0-9]+\^", "\^[0-9][0-9]+"]
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:164: SyntaxWarning: invalid escape sequence '\^'
(RolloutManager pid=3066141)   BAD_REGEXES = ["\^[0-9]+\^", "\^[0-9][0-9]+"]
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:241: SyntaxWarning: invalid escape sequence '\d'
(RolloutManager pid=3066141)   p1 = re.compile("(\d)(,)(\d\d\d)($|\D)")
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:256: SyntaxWarning: invalid escape sequence '\{'
(RolloutManager pid=3066141)   m = re.search("^\\\	ext\{(?P<text>.+?)\}$", expr)
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:289: SyntaxWarning: invalid escape sequence '\^'
(RolloutManager pid=3066141)   expr = re.sub(f"{unit}(es)?(s)? *(\^[0-9]+)?", "", expr)
(RolloutManager pid=3066141) /fast/home/mihir.mahajan/Projects/miles/miles/rollout/rm_hub/math_utils.py:290: SyntaxWarning: invalid escape sequence '\^'
(RolloutManager pid=3066141)   expr = re.sub("\^ *\\\\circ", "", expr)
(RolloutManager pid=3066141) DEBUG: self.generate_rollout loaded
(RolloutManager pid=3066141) DEBUG: self.eval_generate_rollout loaded
(RolloutManager pid=3066141) DEBUG: self.custom_reward_post_process_func initialized to None
(RolloutManager pid=3066141) DEBUG: logger.info for generate_rollout executed
(RolloutManager pid=3066141) DEBUG: logger.info for eval_generate_rollout executed
(RolloutManager pid=3066141) DEBUG: num_gpu_per_engine calculated
(RolloutManager pid=3066141) DEBUG: num_engines calculated
(RolloutManager pid=3066141) DEBUG: self.all_rollout_engines initialized with None placeholders
(RolloutManager pid=3066141) [2025-12-11 19:41:36] rollout.py:74 - import miles.rollout.sglang_rollout.generate_rollout as generate_rollout function.
(RolloutManager pid=3066141) [2025-12-11 19:41:36] rollout.py:76 - import miles.rollout.sglang_rollout.generate_rollout as eval_generate_rollout function.
(pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_JIT_DEEPGEMM_PRECOMPILE is deprecated, please use SGLANG_JIT_DEEPGEMM_PRECOMPILE
(pid=3066905)   warnings.warn(
(pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated, please use SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK
(pid=3066905)   warnings.warn(
(pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(pid=3066905)   import pynvml  # type: ignore[import]
(RolloutManager pid=3066141) [2025-12-11 19:41:47] rollout.py:550 - Ports for engine 0: {'host': '10.86.2.40', 'port': 15000, 'nccl_port': 15001, 'dist_init_addr': '10.86.2.40:15002'}
(SGLangEngine pid=3066905) [2025-12-11 19:41:47] INFO sglang_engine.py:148: Launch HttpServerEngineAdapter at: 10.86.2.40:15000
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/tvm_ffi/_optional_torch_c_dlpack.py:174: UserWarning: Failed to JIT torch c dlpack extension, EnvTensorAllocator will not be enabled.
(SGLangEngine pid=3066905) We recommend installing via `pip install torch-c-dlpack-ext`
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) [2025-12-11 19:41:52] INFO trace.py:52: opentelemetry package is not installed, tracing disabled
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_JIT_DEEPGEMM_PRECOMPILE is deprecated, please use SGLANG_JIT_DEEPGEMM_PRECOMPILE
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated, please use SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(SGLangEngine pid=3066905)   import pynvml  # type: ignore[import]
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/tvm_ffi/_optional_torch_c_dlpack.py:174: UserWarning: Failed to JIT torch c dlpack extension, EnvTensorAllocator will not be enabled.
(SGLangEngine pid=3066905) We recommend installing via `pip install torch-c-dlpack-ext`
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) [2025-12-11 19:42:07] server_args=ServerArgs(model_path='/fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B', tokenizer_path='/fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=True, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='10.86.2.40', port=15000, grpc_mode=False, skip_server_warmup=True, warmups=None, nccl_port=15001, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, mem_fraction_static=0.5, max_running_requests=None, max_queued_requests=None, max_total_tokens=None, chunked_prefill_size=4096, max_prefill_tokens=16384, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=1, hybrid_kvcache_ratio=None, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', device='cuda', tp_size=2, pp_size=1, pp_max_micro_batch_size=None, stream_interval=1, stream_output=False, random_seed=1234, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, dist_timeout=None, download_dir=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, log_level='info', log_level_http=None, log_requests=False, log_requests_level=2, crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=1000, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, otlp_traces_endpoint='localhost:4317', api_key=None, served_model_name='/fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B', weight_version='default', chat_template=None, completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', load_watch_interval=0.1, prefill_round_robin_balance=False, dist_init_addr='10.86.2.40:15002', nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='csgmv', max_lora_chunk_size=16, attention_backend='fa3', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='flashinfer', grammar_backend='xgrammar', mm_attention_backend=None, nsa_prefill_backend='flashmla_sparse', nsa_decode_backend='fa3', speculative_algorithm=None, speculative_draft_model_path=None, speculative_draft_model_revision=None, speculative_draft_load_format=None, speculative_num_steps=None, speculative_eagle_topk=None, speculative_num_draft_tokens=None, speculative_accept_threshold_single=1.0, speculative_accept_threshold_acc=1.0, speculative_token_map=None, speculative_attention_mode='prefill', speculative_moe_runner_backend=None, speculative_ngram_min_match_window_size=1, speculative_ngram_max_match_window_size=12, speculative_ngram_min_bfs_breadth=1, speculative_ngram_max_bfs_breadth=10, speculative_ngram_match_type='BFS', speculative_ngram_branch_length=18, speculative_ngram_capacity=10000000, ep_size=1, moe_a2a_backend='none', moe_runner_backend='auto', flashinfer_mxfp4_moe_precision='default', enable_flashinfer_allreduce_fusion=False, deepep_mode='auto', ep_num_redundant_experts=0, ep_dispatch_algorithm='static', init_expert_location='trivial', enable_eplb=False, eplb_algorithm='auto', eplb_rebalance_num_iterations=1000, eplb_rebalance_layers_per_chunk=None, eplb_min_rebalancing_utilization_threshold=1.0, expert_distribution_recorder_mode=None, expert_distribution_recorder_buffer_size=1000, enable_expert_distribution_metrics=False, deepep_config=None, moe_dense_tp_size=None, elastic_ep_backend=None, mooncake_ib_device=None, max_mamba_cache_size=None, mamba_ssm_dtype='float32', mamba_full_memory_ratio=0.9, enable_hierarchical_cache=False, hicache_ratio=2.0, hicache_size=0, hicache_write_policy='write_through', hicache_io_backend='kernel', hicache_mem_layout='layer_first', hicache_storage_backend=None, hicache_storage_prefetch_policy='best_effort', hicache_storage_backend_extra_config=None, enable_lmcache=False, kt_weight_path=None, kt_method='AMXINT4', kt_cpuinfer=None, kt_threadpool_count=2, kt_num_gpu_experts=None, kt_max_deferred_experts_per_token=None, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, cpu_offload_gb=0, offload_group_size=-1, offload_num_in_group=1, offload_prefetch_step=1, offload_mode='cpu', multi_item_scoring_delimiter=None, disable_radix_cache=False, cuda_graph_max_bs=256, cuda_graph_bs=[1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256], disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_profile_cuda_graph=False, enable_cudagraph_gc=False, enable_nccl_nvls=False, enable_symm_mem=False, disable_flashinfer_cutlass_moe_fp4_allgather=False, enable_tokenizer_batch_encode=False, disable_tokenizer_batch_decode=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, enable_mscclpp=False, enable_torch_symm_mem=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_dp_lm_head=False, enable_two_batch_overlap=False, enable_single_batch_overlap=False, tbo_token_distribution_threshold=0.48, enable_torch_compile=False, enable_piecewise_cuda_graph=False, torch_compile_max_bs=32, piecewise_cuda_graph_max_tokens=4096, piecewise_cuda_graph_tokens=[4, 8, 12, 16, 20, 24, 28, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208, 224, 240, 256, 288, 320, 352, 384, 416, 448, 480, 512, 640, 768, 896, 1024, 1152, 1280, 1408, 1536, 1664, 1792, 1920, 2048, 2176, 2304, 2432, 2560, 2688, 2816, 2944, 3072, 3200, 3328, 3456, 3584, 3712, 3840, 3968, 4096], piecewise_cuda_graph_compiler='eager', torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, triton_attention_split_tile_size=None, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=True, enable_weights_cpu_backup=False, allow_auto_truncate=False, enable_custom_logit_processor=False, flashinfer_mla_disable_ragged=False, disable_shared_experts_fusion=False, disable_chunked_prefix_cache=False, disable_fast_image_processor=False, keep_mm_feature_on_device=False, enable_return_hidden_states=False, enable_return_routed_experts=False, scheduler_recv_interval=1, numa_node=None, enable_deterministic_inference=False, rl_on_policy_target=None, enable_dynamic_batch_tokenizer=False, dynamic_batch_tokenizer_batch_size=32, dynamic_batch_tokenizer_batch_timeout=0.002, debug_tensor_dump_output_folder=None, debug_tensor_dump_layers=None, debug_tensor_dump_input_file=None, debug_tensor_dump_inject=False, disaggregation_mode='null', disaggregation_transfer_backend='mooncake', disaggregation_bootstrap_port=8998, disaggregation_decode_tp=None, disaggregation_decode_dp=None, disaggregation_prefill_pp=1, disaggregation_ib_device=None, disaggregation_decode_enable_offload_kvcache=False, num_reserved_decode_tokens=512, disaggregation_decode_polling_interval=1, custom_weight_loader=[], weight_loader_disable_mmap=False, remote_instance_weight_loader_seed_instance_ip=None, remote_instance_weight_loader_seed_instance_service_port=None, remote_instance_weight_loader_send_weights_group_ports=None, enable_pdmux=False, pdmux_config_path=None, sm_group_num=8, mm_max_concurrent_calls=32, mm_per_request_timeout=10.0, decrypted_config_file=None, decrypted_draft_config_file=None)
(SGLangEngine pid=3066905) [2025-12-11 19:42:08] Using default HuggingFace chat template with detected content format: string
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_JIT_DEEPGEMM_PRECOMPILE is deprecated, please use SGLANG_JIT_DEEPGEMM_PRECOMPILE
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated, please use SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_JIT_DEEPGEMM_PRECOMPILE is deprecated, please use SGLANG_JIT_DEEPGEMM_PRECOMPILE
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated, please use SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_JIT_DEEPGEMM_PRECOMPILE is deprecated, please use SGLANG_JIT_DEEPGEMM_PRECOMPILE
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/sglang/python/sglang/srt/environ.py:299: UserWarning: Environment variable SGL_DISABLE_TP_MEMORY_INBALANCE_CHECK is deprecated, please use SGLANG_DISABLE_TP_MEMORY_INBALANCE_CHECK
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(SGLangEngine pid=3066905)   import pynvml  # type: ignore[import]
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(SGLangEngine pid=3066905)   import pynvml  # type: ignore[import]
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(SGLangEngine pid=3066905)   import pynvml  # type: ignore[import]
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/tvm_ffi/_optional_torch_c_dlpack.py:174: UserWarning: Failed to JIT torch c dlpack extension, EnvTensorAllocator will not be enabled.
(SGLangEngine pid=3066905) We recommend installing via `pip install torch-c-dlpack-ext`
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/tvm_ffi/_optional_torch_c_dlpack.py:174: UserWarning: Failed to JIT torch c dlpack extension, EnvTensorAllocator will not be enabled.
(SGLangEngine pid=3066905) We recommend installing via `pip install torch-c-dlpack-ext`
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) [2025-12-11 19:42:21] INFO trace.py:52: opentelemetry package is not installed, tracing disabled
(SGLangEngine pid=3066905) [2025-12-11 19:42:22 TP0] Init torch distributed begin.
(SGLangEngine pid=3066905) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/tvm_ffi/_optional_torch_c_dlpack.py:174: UserWarning: Failed to JIT torch c dlpack extension, EnvTensorAllocator will not be enabled.
(SGLangEngine pid=3066905) We recommend installing via `pip install torch-c-dlpack-ext`
(SGLangEngine pid=3066905)   warnings.warn(
(SGLangEngine pid=3066905) [2025-12-11 19:42:29 TP1] Init torch distributed begin.
(SGLangEngine pid=3066905) [Gloo] Rank [Gloo] Rank 01 is connected to  is connected to 11 peer ranks.  peer ranks. Expected number of connected peer ranks is : Expected number of connected peer ranks is : 11
(SGLangEngine pid=3066905) 
(SGLangEngine pid=3066905) [Gloo] Rank [Gloo] Rank 01 is connected to  is connected to 11 peer ranks.  peer ranks. Expected number of connected peer ranks is : Expected number of connected peer ranks is : 11
(SGLangEngine pid=3066905) 
(SGLangEngine pid=3066905) [2025-12-11 19:42:29 TP0] sglang is using nccl==2.27.3
(SGLangEngine pid=3066905) [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
(SGLangEngine pid=3066905) [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
(SGLangEngine pid=3066905) [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
(SGLangEngine pid=3066905) [Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
(SGLangEngine pid=3066905) [Gloo] Rank [Gloo] Rank 10 is connected to  is connected to 11 peer ranks.  peer ranks. Expected number of connected peer ranks is : Expected number of connected peer ranks is : 11
(SGLangEngine pid=3066905) 
(SGLangEngine pid=3066905) [2025-12-11 19:42:30 TP0] Init torch distributed ends. mem usage=1.01 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:30 TP1] Init torch distributed ends. mem usage=1.01 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:30 TP0] MOE_RUNNER_BACKEND is not initialized, the backend will be automatically selected
(SGLangEngine pid=3066905) [2025-12-11 19:42:35 TP0] Load weight begin. avail mem=77.66 GB
(SGLangEngine pid=3066905) 
Loading safetensors checkpoint shards:   0% Completed | 0/1 [00:00<?, ?it/s]
(SGLangEngine pid=3066905) 
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.36it/s]
(SGLangEngine pid=3066905) 
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00,  7.35it/s]
(SGLangEngine pid=3066905) 
(SGLangEngine pid=3066905) [2025-12-11 19:42:35 TP0] Load weight end. type=Qwen3ForCausalLM, dtype=torch.bfloat16, avail mem=77.06 GB, mem usage=0.60 GB.
(SGLangEngine pid=3066905) [2025-12-11 19:42:38 TP1] Load weight begin. avail mem=77.66 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP1] Load weight end. type=Qwen3ForCausalLM, dtype=torch.bfloat16, avail mem=77.06 GB, mem usage=0.60 GB.
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP0] Using KV cache dtype: torch.bfloat16
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP1] KV Cache is allocated. #tokens: 715906, K size: 19.12 GB, V size: 19.12 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP0] KV Cache is allocated. #tokens: 715906, K size: 19.12 GB, V size: 19.12 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP1] Memory pool end. avail mem=38.15 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP0] Memory pool end. avail mem=38.15 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP1] Capture cuda graph begin. This can take up to several minutes. avail mem=38.06 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP0] Capture cuda graph begin. This can take up to several minutes. avail mem=38.06 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:39 TP0] Capture cuda graph bs [1, 2, 4, 8, 12, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256]
(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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Capturing batches (bs=96 avail_mem=37.41 GB):  56%|█████▌    | 20/36 [00:03<00:01, 14.99it/s] 
(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
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(SGLangEngine pid=3066905) 
Capturing batches (bs=72 avail_mem=37.39 GB):  67%|██████▋   | 24/36 [00:03<00:00, 16.26it/s]
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(SGLangEngine pid=3066905) 
Capturing batches (bs=56 avail_mem=37.38 GB):  72%|███████▏  | 26/36 [00:03<00:00, 16.72it/s]
Capturing batches (bs=48 avail_mem=37.37 GB):  72%|███████▏  | 26/36 [00:03<00:00, 16.72it/s]
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(SGLangEngine pid=3066905) 
Capturing batches (bs=40 avail_mem=37.36 GB):  78%|███████▊  | 28/36 [00:03<00:00, 16.98it/s]
Capturing batches (bs=32 avail_mem=37.35 GB):  78%|███████▊  | 28/36 [00:03<00:00, 16.98it/s]
Capturing batches (bs=24 avail_mem=37.34 GB):  78%|███████▊  | 28/36 [00:03<00:00, 16.98it/s]
(SGLangEngine pid=3066905) 
Capturing batches (bs=24 avail_mem=37.34 GB):  83%|████████▎ | 30/36 [00:03<00:00, 17.14it/s]
Capturing batches (bs=16 avail_mem=37.34 GB):  83%|████████▎ | 30/36 [00:03<00:00, 17.14it/s]
(SGLangEngine pid=3066905) 
Capturing batches (bs=12 avail_mem=37.33 GB):  83%|████████▎ | 30/36 [00:03<00:00, 17.14it/s]
Capturing batches (bs=12 avail_mem=37.33 GB):  89%|████████▉ | 32/36 [00:03<00:00, 17.47it/s]
Capturing batches (bs=8 avail_mem=37.32 GB):  89%|████████▉ | 32/36 [00:03<00:00, 17.47it/s] 
(SGLangEngine pid=3066905) 
Capturing batches (bs=4 avail_mem=37.31 GB):  89%|████████▉ | 32/36 [00:03<00:00, 17.47it/s]
Capturing batches (bs=4 avail_mem=37.31 GB):  94%|█████████▍| 34/36 [00:03<00:00, 17.78it/s]
Capturing batches (bs=2 avail_mem=37.30 GB):  94%|█████████▍| 34/36 [00:03<00:00, 17.78it/s]
(SGLangEngine pid=3066905) 
Capturing batches (bs=1 avail_mem=37.30 GB):  94%|█████████▍| 34/36 [00:03<00:00, 17.78it/s]
(SGLangEngine pid=3066905) 
Capturing batches (bs=1 avail_mem=37.30 GB): 100%|██████████| 36/36 [00:04<00:00,  9.08it/s]
Capturing batches (bs=1 avail_mem=37.30 GB): 100%|██████████| 36/36 [00:04<00:00,  8.35it/s]
(SGLangEngine pid=3066905) [2025-12-11 19:42:43 TP0] Registering 2052 cuda graph addresses
(SGLangEngine pid=3066905) [2025-12-11 19:42:44 TP0] Capture cuda graph end. Time elapsed: 4.87 s. mem usage=0.77 GB. avail mem=37.29 GB.
(SGLangEngine pid=3066905) [2025-12-11 19:42:44 TP1] Capture cuda graph end. Time elapsed: 4.87 s. mem usage=0.77 GB. avail mem=37.29 GB.
(SGLangEngine pid=3066905) [2025-12-11 19:42:44 TP0] max_total_num_tokens=715906, chunked_prefill_size=4096, max_prefill_tokens=16384, max_running_requests=4096, context_len=40960, available_gpu_mem=37.29 GB
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] INFO:     Started server process [3067087]
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] INFO:     Waiting for application startup.
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] Using default chat sampling params from model generation config: {'repetition_penalty': 1.0, 'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] Using default chat sampling params from model generation config: {'repetition_penalty': 1.0, 'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] The server is fired up and ready to roll!
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] INFO:     Application startup complete.
(SGLangEngine pid=3066905) [2025-12-11 19:42:45] INFO:     Uvicorn running on http://10.86.2.40:15000 (Press CTRL+C to quit)
(SGLangEngine pid=3066905) [2025-12-11 19:42:46 TP0] Prefill batch, #new-seq: 1, #new-token: 1, #cached-token: 0, token usage: 0.00, #running-req: 0, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53436 - "GET /health_generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53436 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP1] Cache flushed successfully!
DEBUG: Created rollout manager.
DEBUG: allocating actor_model
(RolloutManager pid=3066141) DEBUG: self.num_new_engines initialized
(RolloutManager pid=3066141) DEBUG: self.nodes_per_engine calculated
(RolloutManager pid=3066141) DEBUG: self.rollout_engine_lock initialized
(RolloutManager pid=3066141) DEBUG: self._metric_checker initialized
(RolloutManager pid=3066141) DEBUG: self._health_monitor initialized
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53442 - "GET /health HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53460 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53466 - "GET /server_info HTTP/1.1" 404 Not Found
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53466 - "GET /model_info HTTP/1.1" 404 Not Found
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] INFO:     10.86.2.40:53464 - "POST /release_memory_occupation HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] WARNING:  Invalid HTTP request received.
(SGLangEngine pid=3066905) [2025-12-11 19:42:47] WARNING:  Invalid HTTP request received.
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:42:47 TP1] Cache flushed successfully!
(pid=3068344) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(pid=3068344)   import pynvml  # type: ignore[import]
DEBUG: allocated actor_model
DEBUG: critic_model is None
(pid=3068639) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
(pid=3068639)   import pynvml  # type: ignore[import]
(FSDPTrainRayActor pid=3068344) [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:20] train_actor.py:96 - Set NUMA affinity for GPU 0
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:20] actor.py:217 - [Rank 0] Device mesh (2D): world_size=2, cp_size=1, dp_size=2
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:20] actor.py:221 - [Rank 0] Mesh shape: (2, 1), dp_rank=0, cp_rank=0
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:20] actor.py:228 - [Rank 0] Pure DP mode (cp_size=1)
(FSDPTrainRayActor pid=3068344) wandb: Currently logged in as: mihir-mahajan2002 (instant-uv) to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
(FSDPTrainRayActor pid=3068344) wandb: WARNING The `shared` mode feature is experimental and may change. Please contact support@wandb.com for guidance and to report any issues.
(FSDPTrainRayActor pid=3068344) wandb: WARNING Using a boolean value for 'reinit' is deprecated. Use 'return_previous' or 'finish_previous' instead.
(FSDPTrainRayActor pid=3068344) wandb: setting up run zvddkqo0
(FSDPTrainRayActor pid=3068344) wandb: Tracking run with wandb version 0.23.1
(FSDPTrainRayActor pid=3068344) wandb: Run data is saved locally in /fast/home/mihir.mahajan/Projects/miles/wandb/run-20251211_194321-zvddkqo0
(FSDPTrainRayActor pid=3068344) wandb: Run `wandb offline` to turn off syncing.
(FSDPTrainRayActor pid=3068344) wandb: Resuming run qwen3-0.6b-grpo-fsdp_8btmedqp-RANK_0
(FSDPTrainRayActor pid=3068344) wandb: ⭐️ View project at https://wandb.ai/instant-uv/crowd-pilot-miles
(FSDPTrainRayActor pid=3068344) wandb: 🚀 View run at https://wandb.ai/instant-uv/crowd-pilot-miles/runs/zvddkqo0
(SGLangEngine pid=3066905) [2025-12-11 19:43:23] INFO:     10.86.2.40:44772 - "GET /health HTTP/1.1" 200 OK
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:24] actor.py:252 - [Rank 0] tie_word_embeddings=True, loading full model to CPU on all ranks
(FSDPTrainRayActor pid=3068344) Flash Attention 3 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("meta-llama/Llama-3.2-1B", attn_implementation="flash_attention_3", dtype=torch.float16)`
(FSDPTrainRayActor pid=3068344) Flash Attention 3 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("meta-llama/Llama-3.2-1B", attn_implementation="flash_attention_3", dtype=torch.float16)`
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:24] actor.py:1081 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:25] lr_scheduler.py:80 - > learning rate decay style: constant
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:25] checkpoint.py:97 - [FSDP] No tracker file at /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B/latest_checkpointed_iteration.txt; skipping load.
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:25] actor.py:819 - [Rank 1] Creating separate ref model from /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:20] train_actor.py:96 - Set NUMA affinity for GPU 1
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:20] actor.py:217 - [Rank 1] Device mesh (2D): world_size=2, cp_size=1, dp_size=2
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:20] actor.py:221 - [Rank 1] Mesh shape: (2, 1), dp_rank=1, cp_rank=0
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:20] actor.py:228 - [Rank 1] Pure DP mode (cp_size=1)
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:29] actor.py:836 - [Rank 0] Reference model created with FSDP2 CPUOffloadPolicy
(FSDPTrainRayActor pid=3068344) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
(FSDPTrainRayActor pid=3068344)   warnings.warn(  # warn only once
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:29] timer.py:24 - Timer sleep start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:29] memory_utils.py:41 - [Rank 0] Memory-Usage before offload model: {'gpu': '0', 'total_GB': 79.19, 'free_GB': 72.89, 'used_GB': 6.3, 'allocated_GB': 1.11, 'reserved_GB': 2.05}
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:26] actor.py:252 - [Rank 0] tie_word_embeddings=True, loading full model to CPU on all ranks [repeated 3x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)
(FSDPTrainRayActor pid=3068639) Flash Attention 3 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("meta-llama/Llama-3.2-1B", attn_implementation="flash_attention_3", dtype=torch.float16)` [repeated 2x across cluster]
DEBUG: got start_rollout_ids
DEBUG: asserted start_rollout_ids length
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:30] memory_utils.py:41 - [Rank 0] Memory-Usage after offload model: {'gpu': '0', 'total_GB': 79.19, 'free_GB': 74.94, 'used_GB': 4.25, 'allocated_GB': 0.0, 'reserved_GB': 0.0}
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:30] timer.py:32 - Timer sleep end (elapsed: 0.5s)
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:30] timer.py:24 - Timer train_wait start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:26] actor.py:1081 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32 [repeated 3x across cluster]
(FSDPTrainRayActor pid=3068639) set_rollout_manager
(FSDPTrainRayActor pid=3068639) set_rollout_manager done
(FSDPTrainRayActor pid=3068639) [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
DEBUG: set rollout_manager for actor_model
DEBUG: loaded rollout_manager data
DEBUG: returning actor_model and critic_model
DEBUG: Offload rollout: loaded weights.
(RolloutManager pid=3066141) set_train_parallel_config
(RolloutManager pid=3066141) set_train_parallel_config done
(RolloutManager pid=3066141) [2025-12-11 19:43:36] data_source.py:141 - Checkpoint /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B/rollout/global_dataset_state_dict_-1.pt does not exist.
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:26] lr_scheduler.py:80 - > learning rate decay style: constant
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:26] checkpoint.py:97 - [FSDP] No tracker file at /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B/latest_checkpointed_iteration.txt; skipping load.
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:26] actor.py:819 - [Rank 0] Creating separate ref model from /fast/project/HFMI_SynergyUnit/tab_model/huggingface/Qwen3-0.6B
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:29] actor.py:836 - [Rank 1] Reference model created with FSDP2 CPUOffloadPolicy
(FSDPTrainRayActor pid=3068639) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
(FSDPTrainRayActor pid=3068639)   warnings.warn(  # warn only once
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:29] memory_utils.py:41 - [Rank 1] Memory-Usage before offload model: {'gpu': '1', 'total_GB': 79.19, 'free_GB': 72.89, 'used_GB': 6.3, 'allocated_GB': 1.11, 'reserved_GB': 2.05}
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:43:30] memory_utils.py:41 - [Rank 1] Memory-Usage after offload model: {'gpu': '1', 'total_GB': 79.19, 'free_GB': 74.94, 'used_GB': 4.25, 'allocated_GB': 0.0, 'reserved_GB': 0.0}
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35366 - "POST /resume_memory_occupation HTTP/1.1" 200 OK
(FSDPTrainRayActor pid=3068344) set_rollout_manager
(FSDPTrainRayActor pid=3068344) set_rollout_manager done
(FSDPTrainRayActor pid=3068344) [Gloo] Rank 0 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:36] timer.py:24 - Timer update_weights start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:36] update_weight_utils.py:120 - Using flattened tensor bucket
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35376 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35388 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35398 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35406 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35414 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35424 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35438 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35452 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35464 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35480 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35490 - "POST /update_weights_from_tensor HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36] INFO:     10.86.2.40:35506 - "GET /flush_cache HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP0] Cache flushed successfully!
(SGLangEngine pid=3066905) [2025-12-11 19:43:36 TP1] Cache flushed successfully!
DEBUG: Updated actor model weights.
DEBUG: Offload rollout: loaded CUDA graph.
DEBUG: Offload rollout: loaded KV cache.
DEBUG: Defined offload_train function.
DEBUG: Defined onload_rollout function.
Starting rollout 0.
(RolloutManager pid=3066141) DEBUG: generate method started
(RolloutManager pid=3066141) DEBUG: Health monitor started
(RolloutManager pid=3066141) DEBUG: Calling _get_rollout_data
(SGLangEngine pid=3066905) [2025-12-11 19:43:37] INFO:     10.86.2.40:35512 - "POST /resume_memory_occupation HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:37] INFO:     10.86.2.40:35518 - "POST /resume_memory_occupation HTTP/1.1" 200 OK
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:37] timer.py:32 - Timer update_weights end (elapsed: 0.7s)
(RolloutManager pid=3066141) 
Rollout generation:   0%|          | 0/64 [00:00<?, ?it/s]
(SGLangEngine pid=3066905) [2025-12-11 19:43:37 TP0] Prefill batch, #new-seq: 1, #new-token: 199, #cached-token: 0, token usage: 0.00, #running-req: 0, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:43:38 TP0] Prefill batch, #new-seq: 27, #new-token: 4096, #cached-token: 0, token usage: 0.00, #running-req: 1, #queue-req: 36, 
(SGLangEngine pid=3066905) [2025-12-11 19:43:38 TP0] Prefill batch, #new-seq: 37, #new-token: 3242, #cached-token: 2423, token usage: 0.01, #running-req: 27, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:43:42 TP0] Decode batch, #running-req: 64, #token: 65019, token usage: 0.09, cuda graph: True, gen throughput (token/s): 1097.78, #queue-req: 0, 
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:43:36] update_weight_utils.py:120 - Using flattened tensor bucket [repeated 11x across cluster]
(SGLangEngine pid=3066905) [2025-12-11 19:43:47 TP0] Decode batch, #running-req: 64, #token: 129019, token usage: 0.18, cuda graph: True, gen throughput (token/s): 13566.58, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:43:49] INFO:     10.86.2.40:35770 - "POST /generate HTTP/1.1" 200 OK
(FSDPTrainRayActor pid=3068344) set_train_parallel_config
(FSDPTrainRayActor pid=3068344) set_train_parallel_config done
(FSDPTrainRayActor pid=3068639) [Gloo] Rank 1 is connected to 1 peer ranks. Expected number of connected peer ranks is : 1
(SGLangEngine pid=3066905) [2025-12-11 19:43:53 TP0] Decode batch, #running-req: 63, #token: 190018, token usage: 0.27, cuda graph: True, gen throughput (token/s): 10808.94, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:43:54] INFO:     10.86.2.40:35624 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:57] INFO:     10.86.2.40:35668 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:43:59] INFO:     10.86.2.40:36048 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:00] INFO:     10.86.2.40:35892 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:00 TP0] Decode batch, #running-req: 59, #token: 237014, token usage: 0.33, cuda graph: True, gen throughput (token/s): 8912.58, #queue-req: 0, 
(SGLangEngine pid=3066905) [2025-12-11 19:44:00] INFO:     10.86.2.40:35610 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35534 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35584 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35728 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35578 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35748 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35586 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35606 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35546 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35622 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35620 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35554 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35744 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35608 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35698 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35670 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35564 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35576 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35684 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35602 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35688 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35686 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35674 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35672 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35754 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35752 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35750 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35792 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35794 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35802 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35800 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35826 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35828 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35834 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35850 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35846 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35852 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35868 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35874 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35888 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35900 - "POST /generate HTTP/1.1" 200 OK
(RolloutManager pid=3066141) [2025-12-11 19:44:01] sglang_rollout.py:367 - First rollout sample: ['[{\'content\': \'Solve the following math problem step by step. The last line of your response should be of the form Answer: \\\\boxed{$Answer} where $Answer is the answer to the problem.\
\
设 $A B, C D$ 是圆 $O$ 的两条垂直直径, 弦 $D F$ 交 $A B$ 于点 $E, D E=24, E F=18$, 求 $O E$的值。答案的形式是$k \\\\sqrt{m}$，请给出k + m的值。\
\
Remember to put your answer on its own line after "Answer:".\', \'role\': \'user\'}]<think>
Okay, let\'s try to solve this problem step by step. So, we have a circle O with two diameters AB and CD that are perpendicular to each other. Then there\'s a chord DF that intersects AB at point E, and we know DE is 24 units long and EF is 18 units long. The question is asking for the length of OE, and we need to express the answer in the form k√m where k and m are integers, then find k + m.

First, let me visualize the problem. There\'s a circle with two perpendicular diameters AB and CD. So, the circle is divided into four quadrants by these diameters. Points A, B, C, D are all on the circle, and diameters AB and CD intersect at the center O. Now, there\'s a chord DF which intersects AB at point E. The lengths from D to E is 24, and from E to F is 18. So, DF is a chord passing through E, with DE = 24 and EF = 18.

I need to find OE. Let me recall some properties of circles. Since AB and CD are diameters, their centers are at O, which is the midpoint of all diameters. Also, because they are perpendicular, the angle between AB and CD at point O is 90 degrees. But how does this relate to DF?

Maybe I can use coordinate geometry here. Let me set up a coordinate system. Let me place the center O at the origin (0,0). Since AB is a diameter, let me assume AB is along the x-axis for simplicity. Therefore, points A and B would be at (-a, 0) and (a, 0) respectively for some radius a. Similarly, since CD is perpendicular to AB and is also a diameter, CD must be along the y-axis. Therefore, points C and D would be at (0, b) and (0, -b) for some radius b.

Now, the chord DF passes through point E on AB. Let me find coordinates of point E. Since DF is a chord passing through E, which is somewhere on AB. Let me denote coordinates of E as (x, 0). Since DE = 24 and EF = 18, then the coordinates of F must be found accordingly.

Wait, but DF is a chord, so D is at (0, -b), F is on the other side of AB? Wait, no. If E is on AB, and DF intersects AB at E, then F must be on the extended line DF beyond E. Let me think. Since DF is a chord, it must connect two points on the circle. If E is between D and F on AB, then DF is from D to F through E. But since E is on AB, which is diameter, so coordinates of E are (x, 0), and coordinates of F would be (x + (some x component), 0). Wait, maybe coordinates can help here.

Alternatively, maybe using similar triangles or power of a point. Since E lies on AB and DF, perhaps we can use the power of point E with respect to the circle. The power of a point E with respect to circle O is equal to the product of the lengths of the segments from E to the points of intersection of any line through E with the circle. In this case, since DF intersects AB at E and passes through F, which is another point on the circle. Wait, but DF intersects AB at E, so DF passes through E and F, which is also on the circle. So, by power of a point, we have:

Power of E = ED * EF = OE * something?

Wait, power of a point E with respect to circle O is equal to (distance from E to center) squared minus radius squared, which is also equal to ED * EF if E lies inside the circle. Wait, is that correct?

Yes! The power of a point E inside the circle is equal to ED * EF, which can also be expressed as (OE)^2 - r^2, where OE is the distance from E to the center. Therefore, we can set up the equation:

ED * EF = OE^2 - r^2

But since we need to find OE, we need to express ED * EF in terms of OE and r. But let me confirm that.

Let me recall the formula: If a line through E intersects the circle at two points X and Y, then the power of E is EX * EY. Similarly, here, line DF intersects AB at E and goes to F. But wait, does F lie on the circle? Yes, because DF is a chord. So, if DF is a chord, then points D and F are on the circle. Therefore, when we talk about the power of point E, we should consider ED * EF as the power, and equate it to (distance from E to center)^2 - radius^2.

Therefore, let me write that equation:

ED * EF = OE^2 - r^2

But we need to express ED * EF in terms of OE and r. Let me denote OE as d, then OE^2 - r^2 = d^2 - r^2, but we need to relate ED and EF. However, I need to express ED and EF in terms of OE and r.

Alternatively, maybe I need to find coordinates for points D, E, F and then compute the distances or find relations.

Let me try coordinates again. Let me assign coordinates:

Let me take O as (0, 0). Let me assume AB is horizontal diameter, so A is (-a, 0), B is (a, 0). CD is vertical diameter, so C is (0, b), D is (0, -b). Point E is on AB, so coordinates (x, 0). Then, chord DF goes through E, so F is another point on the circle. Since DE = 24, EF = 18, so DF is a chord passing through E with D at (0, -b) and F at some point beyond E. Let me find coordinates of F.

Since E is on AB, which is the x-axis, and DF passes through E, coordinates of F can be found parametrically. Let me parametrize DF. The line DF goes from D (0, -b) through E (x, 0) to F (on the circle). Let me find the coordinates of F.

Let me denote the parameter t such that when t = 0, it\'s at D (0, -b), t = 1, it\'s at E (x, 0), and t = t1, it\'s at F. But maybe another approach.

Alternatively, since we know DE = 24 and EF = 18, then DF = DE + EF = 42. But DF is a chord, so maybe we can find its length in terms of coordinates?

Alternatively, since points D and F are on the circle, coordinates of F satisfy the equation of the circle x^2 + y^2 = r^2. Let me compute coordinates of F. Since DF passes through E, which is on AB.

Let me assume coordinates of E as (x, 0). The line DF passes through (x, 0) and connects D (0, -b) to F. Let me find parametric equations.

Parametric equations: Starting at D (0, -b), moving towards E (x, 0), and then towards F. Let me write parametric equations for the entire line DF.

Let me parameterize the line DF as follows:

Any point on DF can be written as D + t(E - D) = (0, -b) + t(x - 0, 0 - (-b)) = (tx, -b - bt) for t from 0 to 1. At t = 1, we reach E (x, 0). Then, for t > 1, we reach F.

But since F is on the circle, which is x^2 + y^2 = r^2, let me find the coordinates of F when t = something. Wait, but we know that at t = 1, we reach E (x, 0). Let me find the coordinates at t = t1.

So coordinates of F would be (tx1, -b - bt1). Since F is on the circle, then:

(tx1)^2 + (-b - bt1)^2 = r^2.

But we also know that DE = 24 and EF = 18. The distance from D to E is 24, so DE is along the line DF. Let me compute DE. The distance between D (0, -b) and E (x, 0):

DE = sqrt[(x - 0)^2 + (0 - (-b))^2] = sqrt(x^2 + b^2) = 24.

Similarly, EF is 18. Since E is between D and F, so the distance from E to F is 18. Let me express EF in terms of t. The parameter t for E is 1, so from E to F would be t1 - 1, but maybe I need to think differently. Let me compute EF. The coordinates of F are (tx1, -b - bt1). The coordinates of E are (x, 0). So the distance EF can be written as sqrt[(tx1 - x)^2 + (-b - bt1 - 0)^2] = sqrt[(x(t1 - 1))^2 + (-b(1 + t1))^2] = sqrt[(x^2(t1 - 1)^2) + (b^2(1 + t1)^2)].

This equals EF = 18.

But also, since DE = 24, we know that x^2 + b^2 = 24^2 = 576.

Therefore, we have two equations:

1) x^2 + b^2 = 576.

2) sqrt[x^2(t1 - 1)^2 + b^2(1 + t1)^2] = 18.

Additionally, since F is on the circle, we have the equation:

(tx1)^2 + (-b - bt1)^2 = r^2.

But also, the line DF passes through E, so t1 is the parameter for F. However, I don\'t know t1 yet. Maybe there\'s another relation.

Alternatively, since DF is a straight line from D to F, and E is on AB, we can find coordinates of F in terms of t1. But perhaps there\'s a better way. Since we know coordinates of D, E, and know DE and EF, maybe we can find coordinates of F.

Alternatively, let\'s think about similar triangles. Since AB and CD are perpendicular diameters, and DF passes through E on AB, maybe there are similar triangles involved.

Alternatively, since AB and CD are diameters, and they intersect at O, which is the center. The points A, B, C, D are all on the circle. Let me think about triangle EDF. Maybe triangle EDF is a right triangle?

Wait, DF is a chord, and E is on AB. Is there a right angle here? If we can find some right angle, maybe we can use Pythagoras.

But perhaps another approach. Since OE is the distance from O to E, which is on AB, which is a diameter. Let me denote OE as d. Then OE squared is d^2. The radius of the circle is equal to OA, which is half of AB. Let me denote OA as r, so AB = 2r, and CD = 2r as well.

Now, since E is on AB, coordinates of E are (d_o, 0) where d_o is the distance from O to E along AB. Wait, but AB is a diameter, so if O is the origin, then AB is from (-r, 0) to (r, 0). Therefore, E is at (d_o, 0), and OE is |d_o - 0| = d_o. Therefore, OE = d_o, so our goal is to find d_o.

Alternatively, maybe express in terms of r. Let me try.

Let me denote OE = x. So x is the distance from O to E on AB, which is a diameter. Let me assume AB has length 2r, so OA = r, OB = r, etc.

Since E is at distance x from O along AB, coordinates of E are (x, 0). Then, chord DF passes through E. DE = 24, EF = 18, so DF = DE + EF = 42.

Now, points D and F are on the circle. Let me find coordinates of D and F. D is at (0, -r), since CD is a diameter. Let me assume coordinates of D as (0, -r). Then, the line DF goes from (0, -r) through E (x, 0) to F.

Let me find coordinates of F. Let me parametrize the line DF. Let me write parametric equations:

Starting at D (0, -r), moving towards E (x, 0), then towards F. Let me parameterize with parameter t, where t=0 is D, t=1 is E, and t= t1 is F. Then, coordinates of any point on DF can be written as (t * x, -r + t * r). Because from D (0, -r) moving along the line. Let me verify:

At t=0: (0, -r) which is D.

At t=1: (x, -r + r) = (x, 0) which is E.

Yes, that works. Therefore, coordinates of F (which is beyond E) would be (x * t1, -r + r * t1).

Since F is on the circle, substituting into the equation of the circle:

(x * t1)^2 + (-r + r t1)^2 = r^2.

Expanding this:

x² t1² + r² (1 - t1)² = r².

Simplify:

x² t1² + r² (1 - 2t1 + t1²) = r².

Which can be rewritten as:

x² t1² + r² - 2r² t1 + r² t1² = r².

Simplify terms:

Combine like terms:

(x² + r²) t1² - 2r² t1 = 0.

Factor out t1:

t1 [ (x² + r²) t1 - 2r² ] = 0.

Solutions are t1 = 0 (which corresponds to D) and t1 = 2r² / (x² + r²).

Therefore, coordinates of F are:

x_F = x * t1 = x * [ 2r² / (x² + r²) ].

So coordinates of F are ( (2r² x)/(x² + r²), -r + r * (2r² / (x² + r²)) ) = ( (2r² x)/(x² + r²), -r ( (x² + r²) - 2r² ) / (x² + r²) ) = ( (2r² x)/(x² + r²), -r ( - r² ) / (x² + r²) ) = ( (2r² x)/(x² + r²), r³ / (x² + r²) )

Therefore, coordinates of F are ( (2r² x)/(x² + r²), r³ / (x² + r²) )

Now, we can calculate the distance DE and EF, which are given as 24 and 18 respectively.

First, let\'s check DE. DE is the distance from D (0, -r) to E (x, 0). Which is sqrt[ (x - 0)^2 + (0 - (-r))^2 ] = sqrt(x² + r²) = 24.

Therefore, we have:

x² + r² = 24² = 576.

Similarly, let\'s calculate EF. E is (x, 0), and F is ( (2r² x)/(x² + r²), r³ / (x² + r²) ). Let me compute the distance between E and F.

Let me denote the coordinates:

E = (x, 0)

F = ( 2r² x / D, r³ / D ), where D = x² + r².

Therefore, coordinates of F:

x_F = (2r² x)/D, y_F = r³ / D.

So, distance EF is sqrt[ (x_F - x)^2 + (y_F - 0)^2 ].

Let me compute x_F - x:

= [ (2r² x)/D - x ] = x [ (2r²)/D - 1 ]

= x [ (2r² - D)/D ].

Similarly, y_F - 0 = y_F = r³ / D.

Therefore, distance EF squared is:

[ x (2r² - D)/D ]² + [ r³ / D ]²

= [ x² (2r² - D)^2 + r^6 ] / D²

= [ x² (4r^4 - 4r² D + D²) + r^6 ] / D²

Let me compute numerator:

4r^4 x² - 4r² x² D + x² D² + r^6.

But we know from earlier that x² + r² = D. Let me substitute D = x² + r².

Therefore, x² D = x² (x² + r²) = x^4 + x² r².

Similarly, D² = (x² + r²)^2.

Therefore, substituting into the numerator:

4r^4 x² - 4r² (x^4 + x² r²) + x^4 + x² r² + r^6.

Let me compute term by term:

First term: 4r^4 x²

Second term: -4r² x^4 - 4x² r^4

Third term: x^4 + x² r²

Fourth term: + r^6

Combine all terms:

4r^4 x² - 4r² x^4 - 4r^4 x² + x^4 + x² r² + r^6 = [ (4r^4 x² - 4r^4 x²) + (-4r² x^4 + x^4) ) + x² r² + r^6 ]

Simplify:

0 + (-4r² x^4 + x^4) + x² r² + r^6

Factor terms:

x^4 ( -4r² + 1 ) + r² x² + r^6

Hmm, this seems complicated. Let me check if I did the algebra correctly because this seems messy.

Alternatively, maybe there\'s a mistake here. Let me go back.

Wait, perhaps I made'], label: 8, reward: 0
(RolloutManager pid=3066141) 
Rollout generation:  12%|█▎        | 8/64 [00:24<02:48,  3.00s/it]
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35906 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35916 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35938 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35924 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35926 - "POST /generate HTTP/1.1" 200 OK
(RolloutManager pid=3066141) 
Rollout generation:  38%|███▊      | 24/64 [00:24<00:31,  1.27it/s]
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35952 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35968 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35976 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35984 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:01] INFO:     10.86.2.40:35992 - "POST /generate HTTP/1.1" 200 OK
(RolloutManager pid=3066141) 
Rollout generation:  75%|███████▌  | 48/64 [00:24<00:04,  3.26it/s]
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:35994 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36002 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36014 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36028 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36040 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36050 - "POST /generate HTTP/1.1" 200 OK
(RolloutManager pid=3066141) DEBUG: _get_rollout_data returned. Data and metrics obtained.
(RolloutManager pid=3066141) 
Rollout generation: 100%|██████████| 64/64 [00:24<00:00,  2.63it/s]
(RolloutManager pid=3066141) [2025-12-11 19:44:02] sglang_rollout.py:387 - Finish rollout: ['[{\'content\': \'Solve the following math problem step by step. The last line of your response should be of the form Answer: \\\\boxed{$Answer} where $Answer is the answer to the problem.\
\
设有三个函数，第一个是 $y=\\\\varphi(x)$ ，它的反函数是第二个函数，而第三个函数的图象与第二个函数的图象关于 $x+y=0$ 对称。求第三个函数的表达式。答案的形式为 $y = a\\\\varphi(bx)$，请给出 $a + b$ 的值。\
\
Remember to put your answer on its own line after "Answer:".\', \'role\': \'user\'}]<think>
Okay, so I need to solve this problem where there are three functions involved. Let me try to break it down step by step. 

First, there\'s a function y = φ(x), and its inverse is another function. Let me recall that the inverse of a function y = f(x) is found by swapping x and y and solving for y. So, if the original function is y = φ(x), then the inverse function would be x = φ(y), which means y = φ⁻¹(x). The problem says that the inverse of φ(x) is the second function. So I can call that second function ψ(x). So ψ(x) = φ⁻¹(x).

Now, the third function\'s graph is symmetric to the second function\'s graph with respect to the line x + y = 0. That line is the line y = -x. So, the third function\'s graph is the reflection of the second function over the line y = -x. 

Wait, reflection over a line... How do I find the equation of the reflection of a function over a line? Maybe I need to find another function that is the reflection of ψ(x) over y = -x. Let me think. If reflecting a point (a, b) over the line y = -x gives us (-b, a), right? Because reflecting over y = -x flips the coordinates. So if ψ(x) is the inverse function of φ(x), then its graph is the reflection of φ(x)\'s graph over y = -x. 

Alternatively, maybe I can think of the reflection of the graph of a function over y = -x. For example, if we have a function y = f(x), its reflection over y = -x would be y = -f(-x). Wait, is that correct? Let me check. Let\'s take a point (a, b) on the original function. Its reflection over y = -x would be (-b, a). So if (a, b) is (x, y) on the original graph, then (-b, a) is (-y, x) on the reflected graph. Therefore, if the original graph is y = f(x), then the reflected graph would be y = f(-x). Wait, no. Wait, if we have a point (x, y) on the original graph, then its reflection is (-y, x). Therefore, if we want to express that as y in terms of x, we have y = -x when x is the original coordinate, or maybe I need to rephrase.

Wait, suppose we have the original function y = f(x). Let me take a point (a, b) on the original graph. Then, its reflection over y = -x is (-b, a). Therefore, if I want to express this as y = f(x), then I need to switch the roles of variables. Let me think. Suppose the point (a, b) maps to (-b, a). Then, if I want to express this point in terms of the original function\'s parameters, which is in terms of x and y. Let me think in terms of variables.

Let me denote that the original graph has points (x, y). The reflected graph has points (-y, x). So, if I want to express the reflected function in terms of the original variables, it would be y = -x. Wait, no. Let me think of it as a function. Let me consider the reflected graph. For a point (-b, a) on the reflected graph, if we want to find the relation between x and y, then x = -b and y = a. Therefore, if we consider that when the original graph is y = f(x), then for the reflected graph, the relation is x = -y, since y was the original variable. Wait, that seems conflicting. Let me take an example.

Suppose the original function is y = x. Then, the inverse is y = x as well. Reflecting over y = -x... Let me check. The point (0, 0) is on the original function. Its reflection is (0, 0) as well. But if I take another point (1, 1), which is on y = x, its reflection is (-1, 1). Now, if I want to find the equation of the reflected function. Let\'s say the reflected graph is y = -x. Wait, but (1, 1) would not lie on y = -x. Oh, that\'s the problem. So if the original graph is y = x, then the reflected graph should be y = -x? Wait, but (1,1) is not on y = -x. So perhaps my reasoning is off.

Wait, maybe I need to think differently. Let\'s take a general point (x, y) on the original graph, which is y = f(x). Then its reflection over y = -x is ( -y, x ). Therefore, to find the equations of the reflected graph, if we denote some parameter, say, let\'s suppose that the reflected graph is given by y = something. So for each x, there\'s a corresponding y\', which is -y. Wait, perhaps if I reverse the original variables. Let me try again.

Suppose the original graph has a function y = f(x). Its reflection over y = -x would be another function y = g(x) such that for every point (x, y) on g(x), there is a point (y, x) on the original graph. So, solving for y: if (x, y) is on g(x), then y = x. But that\'s the original function. Wait, that can\'t be. So perhaps there\'s confusion here.

Alternatively, maybe the reflection of the graph of y = f(x) over y = -x is given by y = f(-x). Let me check with an example. Take y = x. The reflection over y = -x is y = -x. But (1,1) isn\'t on y = -x. Hmm, maybe that\'s not correct.

Wait, let me think of reflecting the graph of y = x over the line y = -x. Let me find another point. Suppose we have a point (a, b) on y = x. Then, the reflection of this point over y = -x is (-b, a). Let\'s plug into y = -x. The reflected point is (-b, a). Now, if we want to express this as y = something. Let me suppose that the reflected function is y = f(-x). Let\'s check. If x is replaced by -x in y = f(x), then y = f(-x). Let\'s take the original point (a, b) where b = a. Then, the reflected point would be (-a, a). Is (-a, a) equal to f(-x)? Well, if f(x) = x, then f(-x) = -x. But (-a, a) is not (-x, x) unless x = -a. So that seems inconsistent. Therefore, my previous idea is incorrect.

Alternatively, maybe it\'s better to think that if the original graph is y = f(x), then the reflected graph over y = -x is given by y = -f(-x). Let\'s test with the earlier example. Original function y = x, so -f(-x) would be -(-x) = x, which would give y = x, but the reflected point is (-1, 1). Wait, that contradicts. So perhaps that\'s not correct either.

Wait, maybe I need to use coordinates. Suppose the original graph has points (a, b). The reflection over y = -x is (-b, a). So, if we want to express this in terms of x and y, then we have x = -b, y = a. Therefore, if we have x = -y, then y = -x. But then, how does that relate? Wait, maybe not. Let me think of the equation of the reflected function. Let me denote that for the reflected graph, any point (x\', y\') satisfies that (x\', y\') is the reflection of (x, y) over y = -x, so x\' = -y and y\' = x. Therefore, solving for y = -x\' and x = y\'. Therefore, substituting, x = -y\', so y\' = -x. Therefore, the reflected function is y = -x? But that seems contradictory with my previous example. Wait, if the original point is (2, 1), then the reflected point is (-1, 2). If we take y = -x, then x = -y, so for this point, x = -y = -(-1) = 1, which is not the original x-coordinate. So this seems inconsistent.

Wait, maybe I need to think of it as follows. The reflection of the graph of y = f(x) over y = -x is the graph of y = f(-x). Let me check with the point (1, 1). If x = 1, then f(-x) = f(-1) = -1. So the reflected point on this graph would be (1, -1). But the original point was (1, 1). So this suggests that the reflected graph is y = f(-x). Let\'s test another point. Suppose f(x) = x. Then, the reflected graph is y = f(-x) = -x. However, if we take a point (a, b) on f(x), then its reflection ( -b, a ) is on the reflected graph. For this reflected graph, we need to find a function that satisfies x = -y. So if x = -y, then y = -x. Therefore, the reflected function is y = -x. But then why did we get different results with other examples? Wait, for example, if f(x) = x, then the original function is y = x, its inverse is y = x, and its reflection over y = -x is y = -x. So, that works. But if f(x) = x, then the reflected graph is y = -x. Let\'s check a point (1, 1) on the original graph. Its reflection is (-1, 1). Now, if I plug into y = -x, then when x = -1, y = 1. Yes, that works. Therefore, the reflected function is y = -x. But how does this relate to the inverse function?

Wait, maybe there\'s confusion here. Let me recap. The original function is ψ(x), which is the inverse of φ(x). So ψ(x) = φ⁻¹(x). The third function is the reflection of ψ(x) over y = -x. Therefore, if ψ(x) is the inverse function, then its reflection over y = -x would be another function, say, ψ\'. Let me find ψ\'. To find ψ\', we can use the reflection formula. If the original graph is given by ψ(x), then reflecting over y = -x gives us a new function ψ\', which satisfies that for any point (u, v) on ψ\', the point (v, -u) is on ψ(x). 

Let me write down the relationship. For any point (u, v) on ψ\'(x), we have v = -u. Therefore, ψ\'(x) is the function where v = -u, so ψ\'(u) = -v. But since v = -u, substituting, we get ψ\'(u) = -(-u) = u. Therefore, ψ\'(x) = x. Wait, that would mean that the reflection of ψ(x) over y = -x is the identity function, which would imply that ψ\'(x) = x. But that can\'t be because ψ(x) is the inverse function of φ(x). Wait, but if ψ\'(x) = φ(x), then φ(x) = x, meaning that φ is the identity function. But that seems too simple. But maybe I made a mistake here.

Wait, let me double-check. Suppose that the original graph of ψ(x) is such that for each x, ψ(x) = φ⁻¹(x). Then, reflecting over y = -x would mean taking each point (x, ψ(x)) and reflecting it to get (-ψ(x), x). Therefore, if we want to express this as a function, we can write x = -ψ(x), so ψ(x) = -x. Therefore, ψ\' = -x. Therefore, the third function\'s equation is y = -x, or ψ\' has equation y = -x. Therefore, the third function is y = -x. Therefore, the answer would be y = -x, which means a = 0 and b = -1, so a + b = -1. But wait, the problem says the answer should be in the form y = aφ(bx), so if ψ\' is y = -x, then we need to express it in terms of φ. Let\'s see. If the original function was y = φ(x), then the inverse is ψ(x) = φ⁻¹(x). But the reflected function is ψ\'(x) = -x. So we have φ⁻¹(x) = -x. Let me check. If φ⁻¹(x) = -x, then φ(-x) = x. Therefore, φ is the identity function but reflected. Wait, if φ(-x) = x, then φ(x) = -x, so φ would be the negative function. For example, if φ(x) = -x, then its inverse would be ψ(x) = -φ(x) = x, which is the original function. But that contradicts with our previous conclusion. 

Wait, there\'s confusion here. Let me start over. 

We have two functions: φ(x) and its inverse ψ(x). The third function is the reflection of ψ(x) over y = -x. So, if we take any point (a, b) on ψ(x), its reflection is (-b, a). So, to find the function that describes this reflected graph, we can set x = -b and y = a, so x = -ψ(x), so ψ(x) = -x. Therefore, the reflected function is ψ\'(x) = -x. Therefore, the third function is y = -x, which is ψ\'(x). Therefore, in terms of φ, since ψ(x) = φ⁻¹(x), then ψ\'(x) = -x implies that φ⁻¹(x) = -x, so φ(x) = -x. Therefore, φ is the function -x. Therefore, substituting into the answer form y = aφ(bx), we have a = 1, b = -1, so a + b = 0. 

Wait, but the problem says the answer should be in the form y = aφ(bx). If φ is -x, then y = -x = aφ(bx). Comparing, a = 1, b = -1, since φ(bx) would be φ(-x), which is indeed -x. Therefore, the answer would be y = 1 * φ(-x), so a + b = 1 + (-1) = 0. 

But let me check again. Suppose φ(x) = -x, then ψ(x) is the inverse function of φ(x), which would be φ⁻¹(x) = -φ(-x) = -(-x) = x, so ψ(x) = x. Then, reflecting ψ(x) over y = -x: take (x, x) and reflect to (-x, x). The reflected graph would have points (-x, x). If we want to express this as a function, we have y = x, which is the same as φ(x). Therefore, the reflected graph is y = x, which is the same as φ(x). Therefore, the third function is the same as φ(x), which would make a + b = 1 + (-1) = 0. 

But this seems counterintuitive. Let me check another example. Suppose φ(x) is x, then ψ(x) is x. Reflecting over y = -x would take (x, x) to (-x, x). So the reflected graph is y = x, which is the same as φ(x). Therefore, the third function is y = x, which is the same as φ(x). Therefore, in this case, a = 1, b = 1, so a + b = 2. But according to our previous logic, when φ is -x, the third function is y = -x, which is aφ(bx) with a = 1, b = -1, so a + b = 0. 

Wait, but according to the previous example where φ(x) = x, the third function is y = x, which is also aφ(bx) with a = 1, b = 1. So there\'s inconsistency here. Therefore, my reasoning must be flawed. 

Let me think again. The third function is the reflection of ψ(x) over y = -x. So, if ψ(x) is the inverse of φ(x), then ψ\'(x) is the reflection. Let me use coordinate transformation. For a general function ψ(x), reflecting over y = -x gives a new function. Let me recall that reflecting over y = -x can be achieved by replacing x with -x and y with -y. So, if the original function is y = f(x), then after reflection, the new function is y = f(-x). Let me check with our example where ψ(x) is the inverse of φ(x). Suppose ψ(x) is the inverse, so ψ(x) = φ⁻¹(x). Then, if I reflect ψ(x) over y = -x, I get a new function y = f(-x). But since ψ(x) is the inverse of φ(x), then f(-x) is φ(-x). Therefore, ψ\'(x) = φ(-x). Therefore, the third function is y = φ(-x). Therefore, in terms of φ, it would be y = aφ(bx), so a = 1, b = -1, so a + b = 0. 

But this contradicts with our previous example where φ(x) = x, ψ(x) = x, and reflecting over y = -x gives y = x, which is the same as φ(x). Therefore, a + b would be 1 + (-1) = 0. However, if φ(x) = -x, then ψ(x) = x, reflecting over y = -x gives y = -x, which is aφ(bx) with a = 1, b = -1, so a + b = 0. 

But how do we know that the third function is y = φ(-x)? Let me check. Suppose ψ(x) is the inverse of φ(x), so ψ(x) = φ⁻¹(x). Then, reflecting ψ(x) over y = -x gives us a'], label: -2, reward: 0
(RolloutManager pid=3066141) [2025-12-11 19:44:02] sglang_rollout.py:296 - Abort request for ['http://10.86.2.40:15000']
(RolloutManager pid=3066141) [2025-12-11 19:44:02] rollout.py:212 - Save debug rollout data to /fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/qwen3-600M-fsdp-1116-noref/dump_details/rollout_data/0.pt
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36074 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:36066 - "POST /generate HTTP/1.1" 200 OK
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:60568 - "POST /abort_request HTTP/1.1" 200 OK
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Generated rollout data for rollout 0.
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(RolloutManager pid=3066141) DEBUG: _convert_samples_to_train_data called. Data converted.
(RolloutManager pid=3066141) DEBUG: Entering finally block of generate method
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(RolloutManager pid=3066141) DEBUG: generate method finished
(RolloutManager pid=3066141) [2025-12-11 19:44:02] rollout.py:640 - perf 0: {'perf/rollout_time': 25.16530752182007, 'perf/tokens_per_gpu_per_sec': 5141.880340139051, 'perf/longest_sample_tokens_per_sec': 162.76375706708467, 'rollout/response_len/mean': 4043.65625, 'rollout/response_len/median': 4096.0, 'rollout/zero_std/count_0': 5, 'rollout/repetition_frac': 0.0, 'rollout/truncated_ratio': 0.90625}
(SGLangEngine pid=3066905) [2025-12-11 19:44:02] INFO:     10.86.2.40:60582 - "GET /flush_cache HTTP/1.1" 200 OK
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(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:02] timer.py:24 - Timer wake_up start
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(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:09] actor.py:521 - rollout 0: {'rollout/raw_reward': 0.03125, 'rollout/log_probs': -0.4568498730659485, 'rollout/rollout_log_probs': -0.4557011127471924, 'rollout/ref_log_probs': -0.4568498730659485, 'rollout/advantages': 3.725290298461914e-09, 'rollout/returns': 3.725290298461914e-09}
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:09] timer.py:24 - Timer actor_train start
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(FSDPTrainRayActor pid=3068639) `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
(FSDPTrainRayActor pid=3068639) /fast/project/HFMI_SynergyUnit/mihir/micromamba/envs/miles/lib/python3.12/site-packages/torch/utils/checkpoint.py:85: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
(FSDPTrainRayActor pid=3068639)   warnings.warn(
(FSDPTrainRayActor pid=3068344) 
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(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] actor.py:773 - , kl_loss: 0.0000, kl_penalty: 0.0000
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] actor.py:774 - step 0: {'train/loss': 0.0, 'train/pg_loss': 0.0, 'train/pg_clipfrac': 0.0, 'train/ppo_kl': 0.0, 'train/entropy_loss': 0.5995686054229736, 'train/train_rollout_logprob_abs_diff': 0.019154343754053116, 'train/kl_loss': 0.0, 'train/grad_norm': 0.3035055994987488, 'train/lr-pg_0': 1e-06}
(FSDPTrainRayActor pid=3068344) 
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(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] timer.py:32 - Timer actor_train end (elapsed: 11.2s)
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] train_dump_utils.py:13 - Save debug train data to /fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/qwen3-600M-fsdp-1116-noref/dump_details/train_data/0_0.pt
Finished async actor training for rollout 0.
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] timer.py:32 - Timer train end (elapsed: 17.8s)
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] timer.py:24 - Timer train_wait start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] train_metric_utils.py:44 - perf 0: {'perf/sleep_time': 0.45687103271484375, 'perf/update_weights_time': 0.7165162563323975, 'perf/wake_up_time': 0.23313426971435547, 'perf/train_wait_time': 32.469982862472534, 'perf/ref_log_probs_time': 3.4974915981292725, 'perf/log_probs_time': 1.9785676002502441, 'perf/actor_train_time': 11.157981395721436, 'perf/train_time': 17.777125597000122, 'perf/step_time': 50.247108459472656, 'perf/wait_time_ratio': 0.6462059978767046}
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] timer.py:24 - Timer sleep start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:20] memory_utils.py:41 - [Rank 0] Memory-Usage before offload model: {'gpu': '0', 'total_GB': 79.19, 'free_GB': 27.67, 'used_GB': 51.52, 'allocated_GB': 3.68, 'reserved_GB': 46.61}
DEBUG: Offloaded actor model.
Called offload_train for rollout 0.
DEBUG: Onload rollout: loaded weights.
Called onload_rollout for rollout 0.
Traceback (most recent call last):
  File "/fast/home/mihir.mahajan/Projects/miles/train.py", line 140, in <module>
    train(args)
  File "/fast/home/mihir.mahajan/Projects/miles/train.py", line 124, in train
    actor_model.update_weights()
  File "/fast/home/mihir.mahajan/Projects/miles/miles/ray/actor_group.py", line 124, in update_weights
    return ray.get([actor.update_weights.remote() for actor in self._actor_handlers])
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/worker.py", line 2972, in get
    values, debugger_breakpoint = worker.get_objects(
                                  ^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/worker.py", line 1031, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(TypeError): ray::FSDPTrainRayActor.update_weights() (pid=3068639, ip=10.86.2.40, actor_id=748ea1941df52ee320a3f30302000000, repr=<miles.backends.fsdp_utils.actor.FSDPTrainRayActor object at 0x7f456d6a95e0>)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/fast/home/mihir.mahajan/Projects/miles/miles/utils/timer.py", line 78, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/fast/home/mihir.mahajan/Projects/miles/miles/backends/fsdp_utils/actor.py", line 793, in update_weights
    if num_new_engines > 0:
       ^^^^^^^^^^^^^^^^^^^
TypeError: '>' not supported between instances of 'tuple' and 'int'
Traceback (most recent call last):
  File "/fast/home/mihir.mahajan/Projects/miles/train.py", line 140, in <module>
    train(args)
  File "/fast/home/mihir.mahajan/Projects/miles/train.py", line 124, in train
    actor_model.update_weights()
  File "/fast/home/mihir.mahajan/Projects/miles/miles/ray/actor_group.py", line 124, in update_weights
    return ray.get([actor.update_weights.remote() for actor in self._actor_handlers])
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/auto_init_hook.py", line 22, in auto_init_wrapper
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/client_mode_hook.py", line 104, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/worker.py", line 2972, in get
    values, debugger_breakpoint = worker.get_objects(
                                  ^^^^^^^^^^^^^^^^^^^
  File "/home/mihir.mahajan/.local/lib/python3.12/site-packages/ray/_private/worker.py", line 1031, in get_objects
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(TypeError): ray::FSDPTrainRayActor.update_weights() (pid=3068639, ip=10.86.2.40, actor_id=748ea1941df52ee320a3f30302000000, repr=<miles.backends.fsdp_utils.actor.FSDPTrainRayActor object at 0x7f456d6a95e0>)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/fast/home/mihir.mahajan/Projects/miles/miles/utils/timer.py", line 78, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/fast/home/mihir.mahajan/Projects/miles/miles/backends/fsdp_utils/actor.py", line 793, in update_weights
    if num_new_engines > 0:
       ^^^^^^^^^^^^^^^^^^^
TypeError: '>' not supported between instances of 'tuple' and 'int'
(SGLangEngine pid=3066905) [2025-12-11 19:44:22] INFO:     10.86.2.40:54656 - "POST /resume_memory_occupation HTTP/1.1" 200 OK
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:22] memory_utils.py:41 - [Rank 0] Memory-Usage after offload model: {'gpu': '0', 'total_GB': 79.19, 'free_GB': 73.88, 'used_GB': 5.31, 'allocated_GB': 0.35, 'reserved_GB': 0.39}
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:22] timer.py:32 - Timer sleep end (elapsed: 1.8s)
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:22] timer.py:24 - Timer update_weights start
(FSDPTrainRayActor pid=3068344) [2025-12-11 19:44:22] timer.py:32 - Timer update_weights end (elapsed: 0.0s)
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:22] selector_events.py:1068 - socket.send() raised exception.
(SGLangEngine pid=3066905) [2025-12-11 19:44:23] INFO:     10.86.2.40:54666 - "GET /health HTTP/1.1" 200 OK
[2025-12-11 19:44:23] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:23] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:23] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:23] selector_events.py:1068 - socket.send() raised exception.
wandb: 
wandb: 🚀 View run qwen3-0.6b-grpo-fsdp_8btmedqp-RANK_0 at: https://wandb.ai/instant-uv/crowd-pilot-miles/runs/zvddkqo0
wandb: Find logs at: ../../../../fast/home/mihir.mahajan/Projects/miles/wandb/run-20251211_194109-zvddkqo0/logs
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:44:20] train_dump_utils.py:13 - Save debug train data to /fast/project/HFMI_SynergyUnit/tab_model/huggingface/shared_data/qwen3-600M-fsdp-1116-noref/dump_details/train_data/0_1.pt
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:44:20] memory_utils.py:41 - [Rank 1] Memory-Usage before offload model: {'gpu': '1', 'total_GB': 79.19, 'free_GB': 28.49, 'used_GB': 50.7, 'allocated_GB': 3.68, 'reserved_GB': 45.79}
(FSDPTrainRayActor pid=3068639) [2025-12-11 19:44:22] memory_utils.py:41 - [Rank 1] Memory-Usage after offload model: {'gpu': '1', 'total_GB': 79.19, 'free_GB': 73.88, 'used_GB': 5.31, 'allocated_GB': 0.35, 'reserved_GB': 0.39}
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
2025-12-11 19:44:31,515	ERR cli.py:73 -- ---------------------------------------
2025-12-11 19:44:31,518	ERR cli.py:74 -- Job 'raysubmit_Vg4BLu1rdFx1T4Qv' failed
2025-12-11 19:44:31,520	ERR cli.py:75 -- ---------------------------------------
2025-12-11 19:44:31,523	INFO cli.py:88 -- Status message: Job entrypoint command failed with exit code 1, last available logs (truncated to 20,000 chars):
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.
[2025-12-11 19:44:25] selector_events.py:1068 - socket.send() raised exception.