From bb3ce4d9e4f873d6c30aec6795efd275e9e8534d Mon Sep 17 00:00:00 2001 From: gspark Date: Sat, 24 Oct 2026 01:35:49 +0900 Subject: [PATCH 1/4] exp: connect matched HTTP benchmark to assigned ATOM and RNGD workers (#248) --- opt_prime/examples/pd/http_matrix/README.md | 10 + opt_prime/examples/pd/http_matrix/STATUS.md | 2 + .../examples/pd/http_matrix/atom_server.py | 74 + .../examples/pd/http_matrix/benchmark.py | 42 +- .../atom248-qwen06b-native-a.json | 9528 +++++++++++++++++ .../atom248-qwen06b-pd-a.json | 8394 +++++++++++++++ .../evidence248-heterogeneous/events.jsonl | 4 + ...2026-10-24-heterogeneous-performance248.md | 42 + .../examples/pd/http_matrix/test_benchmark.py | 31 + 9 files changed, 18125 insertions(+), 2 deletions(-) create mode 100644 opt_prime/examples/pd/http_matrix/atom_server.py create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-native-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-pd-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl create mode 100644 opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md diff --git a/opt_prime/examples/pd/http_matrix/README.md b/opt_prime/examples/pd/http_matrix/README.md index d573a5fd..ad854382 100644 --- a/opt_prime/examples/pd/http_matrix/README.md +++ b/opt_prime/examples/pd/http_matrix/README.md @@ -73,3 +73,13 @@ Where the SIGPIPE candidate is needed, pass `--http-module /tmp/pd-http-safe.py` Both caches are disabled and producer/local chunks are256. Warmup, loading and separate token-ID checks are excluded from timed batches. JSON retains raw per-request SSE events' first-arrival latency, E2E latency, estimated TPOT, reconstructed text equality, whole-batch throughput and final server metrics. SSE events can coalesce tokens: first-event latency and estimated TPOT are not per-token engine timings. `passed` records workflow/count success; `parity_passed` additionally requires reference token/text equality. A parity failure saves evidence and exits unsuccessfully. Repeat modes in reverse order against the same native reference and do not run CPU regression suites concurrently with final measurements. These Torch PD baselines gather and transfer the whole KV after prefill; they do not implement within-request compute/copy overlap. Existing HTTP transfer histograms do not observe the batched worker timing path; zero observations do not mean zero transfer cost. See issue249 for separate accumulated worker duration counters. + +### External backend comparison + +`benchmark.py --server-config config.json` uses the same client requests, reference checks and timing loop with explicitly supplied server commands. The file is trusted executable configuration: `servers` is a nonempty list of argv arrays (no shell expansion), each containing exactly one standalone `{port}` argument. `metadata` describes physical resources and execution policies; it replaces GPU-specific defaults rather than implying a GPU configuration for an NPU server. Only supported resource fields are accepted. + +`atom_server.py` connects one assigned ATOM to its existing B1/context9216 artifact. Omit `--worker-python`/`--engine-factory` for native ATOM; supply both for the established RNGD subprocess producer. Pass explicit trusted module candidates and artifact root. The allocator/container bootstrap must restrict visible devices; the server also rejects anything other than one ATOM node. Requests in this benchmark use `ignore_eos=true`, so fixed output lengths are comparable. Client concurrency above one is queued work, not physical batch size. + +For slow cold initialization, pass `--startup-timeout 1800` explicitly. Default180 remains unchanged; nonpositive values fail before model loading. Startup is excluded from measured latency/throughput. In the first hardware attempt, the shorter outer timeout terminated initialization before the existing subprocess worker's1800second deadline; that failure is retained alongside the passing nativeATOM baseline. A longer deadline is not evidence that initialization or PD succeeds. + +The initial Qwen3-0.6B nativeATOM run passed12batches at inputs128/8192,output32,concurrency1/4. RNGD-to-ATOM performance/parity remains under validation. Use the experiment record for pinned runtime modules, environment and commands. diff --git a/opt_prime/examples/pd/http_matrix/STATUS.md b/opt_prime/examples/pd/http_matrix/STATUS.md index d495f912..bcd04394 100644 --- a/opt_prime/examples/pd/http_matrix/STATUS.md +++ b/opt_prime/examples/pd/http_matrix/STATUS.md @@ -39,3 +39,5 @@ A real terminated subprocess producer previously left /ready200 and new requests Four models times native/PD/DP2 completed144measured batches with parity. Inputs128/8192, outputs32, concurrency1/4, three repeats. DP2 leads all four at8192/c4. BlockA is ordered and has a short concurrent CPU-test window during KONI; quiet reversed-order replication and longer outputs remain. No within-request compute/copy overlap in this Torch baseline. See evidence248 and matched-pd-performance248 record. BlockB reverse-model/mode repetition completed144additional batches with parity (288A/B total). Same-two-GPU DP2 leads all four at8192/c4. PD relative to native is variable: Gemma23.11->17.79tok/s,Qwen0.6B22.37->18.41,Qwen4B13.25->15.12,KONI14.47->14.02. No stable native-to-PD speedup claim. Runtime273952a adds stage counters; scheduling/compute unchanged. Longer-output and actual heterogeneous benchmarking ongoing. + +External-backend benchmark candidate:trusted argv config and explicit startup deadline. Real ATOM nativeQwen0.6B12batches pass parity; firstPDattempt timed out at180seconds before readiness, raw failure retained. Retry with1800seconds is live. No heterogeneous speedup claim.3harness tests pass; no core serving changes. diff --git a/opt_prime/examples/pd/http_matrix/atom_server.py b/opt_prime/examples/pd/http_matrix/atom_server.py new file mode 100644 index 00000000..fe0e51a6 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/atom_server.py @@ -0,0 +1,74 @@ +"""Single assigned ATOM HTTP backend, optionally fed by the established RNGD subprocess adapter.""" +import argparse +import importlib.util +import signal +import sys +from pathlib import Path + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--model', required=True) + p.add_argument('--cache-dir', required=True) + p.add_argument('--port', required=True, type=int) + p.add_argument('--batching', required=True, help='trusted ATOM batching module') + p.add_argument('--http-module', required=True, help='trusted HTTP module') + p.add_argument('--pd-module', required=True, help='trusted subprocess worker module') + p.add_argument('--worker-python') + p.add_argument('--engine-factory') + p.add_argument('--width', type=int, default=8192) + p.add_argument('--kv-blocks', type=int, default=3) + p.add_argument('--max-output', type=int, default=128) + a = p.parse_args() + if bool(a.worker_python) != bool(a.engine_factory): + p.error('worker-python and engine-factory must be supplied together') + for name, path in [('opt_prime.accel.pd.pd_batching', a.pd_module), + ('opt_prime.accel.platforms.atom.batching', a.batching), + ('opt_prime.accel.serve.llmd', a.http_module)]: + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module + spec.loader.exec_module(module) + from opt_prime.accel.pd.pd_batching import SubprocessPrefillWorker + from opt_prime.accel.platforms.atom.engine import AtomRblnEngine + from opt_prime.accel.serve.batched import BatchedOwner, BatchedService + from opt_prime.accel.serve.llmd import serve_stage + from transformers import AutoTokenizer + + nodes = list(Path('/dev').glob('rbln[0-9]*')) + if len(nodes) != 1: + raise RuntimeError('benchmark requires exactly one assigned ATOM device') + engine = AtomRblnEngine(devices=[int(nodes[0].name[4:])], max_seq_len=9216, + batch_size=1, kv_blocks=a.kv_blocks, cache_dir=a.cache_dir) + worker = owner = server = http = None + try: + engine.start(a.model) + c = engine.model.config.get_text_config() + if a.worker_python: + worker = SubprocessPrefillWorker(a.worker_python, + ['--model', a.model, '--width', str(a.width), '--engine-factory', a.engine_factory], + slots=1, layers=c.num_hidden_layers, kv_heads=c.num_key_value_heads, + head_dim=getattr(c, 'head_dim', None) or c.hidden_size // c.num_attention_heads, + max_context=a.width) + tok = AutoTokenizer.from_pretrained(a.model, local_files_only=True) + batcher = engine.batcher(prefix_cache=False, prefill_worker=worker, local_prefill=worker is None) + service = BatchedService(batcher, model=a.model, vocab_size=c.vocab_size, + max_output=a.max_output, tokenizer=tok, + render=lambda ids: tok.decode(ids, skip_special_tokens=True)) + owner = BatchedOwner(service) + # The benchmark uses fixed-length ignore_eos requests; no EOS-dependent timing comparisons. + signal.signal(signal.SIGTERM, lambda *_: owner.submit('stop')) + server, http = serve_stage(owner, 'decode', a.port) + owner.run() + finally: + if server: + server.shutdown() + server.server_close() + http.join(5) + if worker: + worker.close() + engine.close() + + +if __name__ == '__main__': + main() diff --git a/opt_prime/examples/pd/http_matrix/benchmark.py b/opt_prime/examples/pd/http_matrix/benchmark.py index 72434541..8c273ba2 100644 --- a/opt_prime/examples/pd/http_matrix/benchmark.py +++ b/opt_prime/examples/pd/http_matrix/benchmark.py @@ -28,11 +28,35 @@ def timing_summary(rows, wall_s): } +RESOURCE_FIELDS = {"physical_gpus", "physical_atom", "physical_rngd", "slots_per_decoder", + "producer_cache", "decoder_cache", "producer_chunk", "local_chunk", "context"} + + +def load_server_config(path): + """Trusted executable argv arrays; no shell expansion. Resource facts are supplied by the experiment.""" + config = json.loads(Path(path).read_text()) + if not isinstance(config, dict): + raise TypeError("server config must be an object") + servers, metadata = config.get("servers"), config.get("metadata") + if not isinstance(servers, list) or not servers or not isinstance(metadata, dict): + raise ValueError("server config needs nonempty servers and metadata object") + if set(metadata) - RESOURCE_FIELDS: + raise ValueError("unsupported resource metadata key") + for argv in servers: + if not isinstance(argv, list) or not argv or not all(isinstance(x, str) and x for x in argv): + raise ValueError("each server must be a nonempty argv array") + if argv.count("{port}") != 1: + raise ValueError("each server needs exactly one standalone {port} argument") + return config + + def main(): p = argparse.ArgumentParser(description=__doc__) p.add_argument("--model", required=True) p.add_argument("--mode", choices=["native", "dp2", "pd"], required=True) p.add_argument("--output", required=True) + p.add_argument("--startup-timeout", type=int, default=180, help="server readiness deadline in seconds; excluded from timing") + p.add_argument("--server-config", help="trusted JSON argv templates and resource metadata for external backends") p.add_argument("--reference", help="native benchmark JSON with identical token-ID prompts") p.add_argument("--http-module", help="trusted local HTTP candidate") p.add_argument("--lengths", nargs="+", type=int, default=[128, 8192]) @@ -42,6 +66,9 @@ def main(): p.add_argument("--slots", type=int, default=4) p.add_argument("--dtype", choices=["bfloat16", "float32"], default="bfloat16") a = p.parse_args() + if a.startup_timeout <= 0: + p.error("startup timeout must be positive") + external = load_server_config(a.server_config) if a.server_config else None if min(*a.lengths, *a.outputs, *a.concurrency, a.repeats, a.slots) < 1: p.error("sizes must be positive") if max(a.lengths) + max(a.outputs) > 9216: @@ -77,10 +104,19 @@ def main(): "local_chunk": 256, "context": 9216, "prompts": prompts, "warmups": [], "correctness_batches": [], "batches": [], "harness_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(), + "startup_timeout_s": a.startup_timeout, "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. Loading/warmup/correctness checks excluded."} + devices = ["cuda:0", "cuda:1"] if a.mode == "dp2" else ["cuda:1"] + if external: + devices = [None] * len(external["servers"]) + for key in RESOURCE_FIELDS: + result.pop(key, None) + result.update(external["metadata"]) + result["replicas"] = len(devices) + result["server_config"] = external started = time.perf_counter() try: - for endpoint, device in enumerate(["cuda:0", "cuda:1"] if a.mode == "dp2" else ["cuda:1"]): + for endpoint, device in enumerate(devices): with socket.socket() as sock: sock.bind(("127.0.0.1", 0)) port = sock.getsockname()[1] @@ -95,11 +131,13 @@ def main(): if a.mode == "pd": cmd += ["--pd-prefill-device", "cuda:0", "--pd-prefill-no-prefix-cache", "--pd-prefill-chunk", "256"] + if external: + cmd = [str(port) if arg == "{port}" else arg for arg in external["servers"][endpoint]] proc = subprocess.Popen(cmd, stdout=log, stderr=subprocess.STDOUT, start_new_session=True, env=dict(os.environ, OMP_NUM_THREADS="4", MKL_NUM_THREADS="4")) procs.append(proc) ports.append(port) - end = time.monotonic() + 180 + end = time.monotonic() + a.startup_timeout while True: if proc.poll() is not None: raise RuntimeError(f"server{endpoint} exited{proc.returncode}; see server log") diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-native-a.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-native-a.json new file mode 100644 index 00000000..9c824185 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-native-a.json @@ -0,0 +1,9528 @@ +{ + "passed": true, + "model": "Qwen3-0.6B", + "mode": "native", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, 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The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue" + }, + "8192:32": { + "tokens": [ + 12095, + 198, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303 + ], + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue" + } + }, + "warmup_and_checks_s": 9.943625851068646, + "metrics": [ + "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Qwen3-0.6B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Qwen3-0.6B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Qwen3-0.6B\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-0.6B\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-0.6B\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.02\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.04\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.06\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.08\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.25\"} 13\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.75\"} 21\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 21\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.5\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"5.0\"} 38\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"7.5\"} 42\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"10.0\"} 42\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-0.6B\"} 62.75388323806692\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-0.6B\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 21\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.025\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.05\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.15\"} 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histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.3\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 13\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.8\"} 17\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 21\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.5\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.5\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"5.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"10.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"15.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"20.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"30.0\"} 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0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-0.6B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-0.6B\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# 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DP2 remains ahead at8192/c4; PD relative native varies. Started GPU output128/c8 queue and actual isolated ATOM native benchmark on separate hardware.", "next": "Monitor both live jobs; nativeATOM baseline then real RNGD-to-ATOM PD with identical fixed-output prompts.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/perf248-summary-b-longc4.json", "size": 997, "sha256": "80e2c7920e21fbff"}], "experiments": ["experiments/2026-10-24-matched-pd-performance248.md", "experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} +{"id": "7987a83b7679464f96e245634ff1f5e3", "ts": "2026-10-24T01:25:53+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Actual ATOM native Qwen3-0.6B fixed-output benchmark completed12batches with parity; launched live RNGD-to-ATOM against identical native reference. GPU long128 queue remains active separately.", "next": "Inspect heterogeneous PD output parity and performance; expand supported model artifacts and retain failures.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"model": "Qwen3-0.6B", "backend": "atom", "output_tokens": "32", "input_tokens": "128,8192", "concurrency": "1,4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen06b-native-a.json", "size": 139194, "sha256": "a17f3e37ab3cec64"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} +{"id": "a06bf63de3974c3a98104fbd63867f33", "ts": "2026-10-24T01:30:43+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Heterogeneous PD attempt failed before measurement at outer180s startup deadline, versus worker1800s bound. Preserved failure; added explicit positive --startup-timeout with3tests and retried under1800s. GPU Qwen0.6B native output128/c8 passed; PD ongoing.", "next": "Track same heterogeneous retry and inspect initialization progress; finish GPU extended matrix; no PD speedup claim until parity/timing available.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"model": "Qwen3-0.6B", "route": "RNGD-ATOM", "startup_timeout_s": "1800"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen06b-pd-a.json", "size": 98674, "sha256": "2fff4ddcbfbb8716"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} +{"id": "2651ff872dd84956ac5ebf56212b0f34", "ts": "2026-10-24T01:33:41+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Qwen3-0.6B output128 extension:54batches all parity pass;8192input/c8 native65.74,PD74.66,DP2120.65tok_s. Qwen4B native started. RNGD retry confirmed live CPU-active initialization at3min; readiness pending.", "next": "Continue both existing jobs; collect Qwen4B long-output results and RNGD readiness without duplicate launches.", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "74.6554639496"}, "params": {"model": "Qwen3-0.6B", "output_tokens": "128", "input_tokens": "8192", "concurrency": "8", "mode": "pd"}, "artifacts": [{"path": "analysis/serving-matrix-201/perf248-qwen06b-long128-summary.json", "size": 1939, "sha256": "cc8f91ae525cc8da"}], "experiments": ["experiments/2026-10-24-matched-pd-performance248.md", "experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md new file mode 100644 index 00000000..6e0bdb0d --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md @@ -0,0 +1,42 @@ +# Fixed-output ATOM and RNGD-to-ATOM HTTP comparison + +- **Date**: 2026-10-24 +- **Task**: aicomp #248 +- **Host / device**: RNGD+ATOM host, one scheduler-assigned RNGD and ATOM; ATOM B1 +- **Commit**: aba3ad2 plus uncommitted external-server config/ATOM launcher in aicomp-heteroperf-248; runtime core candidates pd_batching238/atom_batching236/llmd214 + +## Question + +Compare nativeATOM and real RNGD-prefill/ATOM-decode using identical token-ID prompts, fixed output lengths and the same HTTP measurement client. + +## Setup + +Isolated experiment Podperf248 in codex-serving-matrix-201, deadline7200seconds,128GiB memory. Reuse validated PYTHONUSERBASE/ATOM bootstrap and cached context9216artifacts. Server asserts exactly one exposed ATOM and bootstrap checks actual opened descriptors. Qwen3-0.6B first, input128/8192,output32,client concurrency1/4,three repeats. Physical batch1; concurrent clients queue. Prefix caches off. Producer factorylong_width_variant:make_prefill,width8192whenPD. Do not infer HWbatch4. + +## Command + +From the allocated container's scratch directory: + +```bash +bash run-benchmark248.sh --model Qwen/Qwen3-0.6B --mode native --server-config atom248-qwen06b-native-config.json --lengths 128 8192 --outputs 32 --concurrency 1 4 --repeats 3 --slots 1 --output atom248-qwen06b-native-a.json +``` + +The external-server config uses atom_server248.py through the existing container_bootstrap.py and ATOM interpreter; scripts are copied from aicomp-heteroperf-248. Configs preserved in /tmp/perf248-configs locally and experiment scratch remotely. PD command additionally selectsPDconfig and uses the native JSON as--reference. Current native launch is live; no performance result yet. + +## Results + +External-config red/green validation:1failed/1passed before implementation;2passed after, Ruff clean. ATOM server started and bootstrap confirmed only its assigned device opened. Full native benchmark result pending. This startup is not a successful throughput/parity claim. + +## Conclusion + +Fixed-output heterogeneous benchmark is connected to actual assigned hardware. Finish native, compare PD, retain any parity failure, then expand to other models. Release only the owned Pod when the runs end. + +NativeATOM Qwen3-0.6B completed12batches,passed=true,parity_passed=true. RawJSON copied to analysis/serving-matrix-201/atom248-qwen06b-native-a.json. Started identical PDcommand with --mode pd --server-config atom248-qwen06b-pd-config.json --reference atom248-qwen06b-native-a.json --output atom248-qwen06b-pd-a.json. PD result pending. + +## PD startup failure and corrected benchmark deadline + +Initial PD run exited1 with TimeoutError("server startup") at the benchmark's180second readiness deadline; zero timing batches completed. ATOM was bound correctly; producer was still starting. SubprocessPrefillWorker's existing startup_timeout is1800seconds, so the outer benchmark terminated initialization earlier than the worker's intended bound. The reason for slow producer initialization is not yet proven. Resource-tracker warning reported cleanup of one shared-memory object after group termination; subsequent process inspection found no worker/controller from that run. Failed JSON retained as analysis/serving-matrix-201/atom248-qwen06b-pd-a.json and server log on the accelerator scratch directory. + +Added --startup-timeout (default180 unchanged forGPU), positive-value validation, and recorded configured deadline inJSON. Red test1failed/2passed; fixed3passed in0.08s; Ruff clean after explicitcheck=False in the CLI negative test. RetriedPD with --startup-timeout1800 and output atom248-qwen06b-pd-b.json, retaining the original failure. Loading remains excluded from measured throughput. The owned Pod remains allocated with its original deadline; retry is live and no successful PD claim is made yet. + +Live retry observation: producer process still active after3m05s, averageCPU770% andRSS~12.7GiB, server waiting for producer readiness. This demonstrates a live CPU-active initialization, not successful completion or proof of the exact slow stage. Worker source loads weights toFP32 then constructs the compiled width8192engine before sendingready. Source also pads shorter prompts to compiledwidth; benchmark must not generalize fixed8192short-prompt costs to a width-dispatched implementation. No restart performed. diff --git a/opt_prime/examples/pd/http_matrix/test_benchmark.py b/opt_prime/examples/pd/http_matrix/test_benchmark.py index 5109e8bd..d68bdb0d 100644 --- a/opt_prime/examples/pd/http_matrix/test_benchmark.py +++ b/opt_prime/examples/pd/http_matrix/test_benchmark.py @@ -14,3 +14,34 @@ def test_batch_accounting_and_parity(): assert out["median_tpot_est_s"] == pytest.approx(.15) assert out["median_e2e_s"] == pytest.approx(.6) assert out["all_text_equal"] is False + + +def test_external_server_config_requires_port_and_metadata(tmp_path): + import json + + import benchmark + + path = tmp_path / 'server.json' + good = {'servers': [['python', 'atom_server.py', '--port', '{port}', '--model', 'a model']], + 'metadata': {'physical_gpus': 0, 'physical_atom': 1, 'physical_rngd': 1, + 'producer_chunk': None}} + path.write_text(json.dumps(good)) + assert benchmark.load_server_config(path) == good + for bad in [[], {'servers': [['python', '{port}']], 'metadata': {'passed': True}}, {}, {'servers': []}, {'servers': [['python', 'server.py']], 'metadata': {}}, + {'servers': ['python server.py {port}'], 'metadata': {}}, + {'servers': [['python', '{port}']], 'metadata': []}]: + path.write_text(json.dumps(bad)) + with pytest.raises((ValueError, TypeError)): + benchmark.load_server_config(path) + + +def test_startup_timeout_rejects_nonpositive_before_loading_model(): + import subprocess + import sys + from pathlib import Path + + run = subprocess.run([sys.executable, str(Path(__file__).with_name('benchmark.py')), + '--model', 'unused', '--mode', 'native', '--output', 'unused.json', + '--startup-timeout', '0'], check=False, capture_output=True, text=True) + assert run.returncode == 2 + assert 'startup timeout must be positive' in run.stderr From 5d3effa7a19275e5e6fcf185930409f3f2116ba7 Mon Sep 17 00:00:00 2001 From: gspark Date: Sat, 24 Oct 2026 01:41:22 +0900 Subject: [PATCH 2/4] exp: record real RNGD-to-ATOM fixed-output performance (#248) --- opt_prime/examples/pd/http_matrix/STATUS.md | 2 + .../atom248-qwen06b-pd-b.json | 9533 +++++++++++++++++ .../atom248-qwen06b-summary.json | 66 + ...2026-10-24-heterogeneous-performance248.md | 21 + 4 files changed, 9622 insertions(+) create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-pd-b.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-summary.json diff --git a/opt_prime/examples/pd/http_matrix/STATUS.md b/opt_prime/examples/pd/http_matrix/STATUS.md index bcd04394..2fa16e7b 100644 --- a/opt_prime/examples/pd/http_matrix/STATUS.md +++ b/opt_prime/examples/pd/http_matrix/STATUS.md @@ -41,3 +41,5 @@ Four models times native/PD/DP2 completed144measured batches with parity. Inputs BlockB reverse-model/mode repetition completed144additional batches with parity (288A/B total). Same-two-GPU DP2 leads all four at8192/c4. PD relative to native is variable: Gemma23.11->17.79tok/s,Qwen0.6B22.37->18.41,Qwen4B13.25->15.12,KONI14.47->14.02. No stable native-to-PD speedup claim. Runtime273952a adds stage counters; scheduling/compute unchanged. Longer-output and actual heterogeneous benchmarking ongoing. External-backend benchmark candidate:trusted argv config and explicit startup deadline. Real ATOM nativeQwen0.6B12batches pass parity; firstPDattempt timed out at180seconds before readiness, raw failure retained. Retry with1800seconds is live. No heterogeneous speedup claim.3harness tests pass; no core serving changes. + +Qwen0.6B actual RNGD-to-ATOM completed12PD batches with parity against nativeATOM;42remote/0localprefills. Readiness369.73s excluded from timing.8192input/c4 throughputnative22.25vsPD10.58tok/s. Current width8192producer is slower; shorter prompts also pad to8192. Other three model comparisons running. Failed180s startup record retained. diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-pd-b.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-pd-b.json new file mode 100644 index 00000000..c02664cd --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-pd-b.json @@ -0,0 +1,9533 @@ +{ + "passed": true, + "model": "Qwen3-0.6B", + "mode": "pd", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, 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The blue", + "text_equal": true, + "first_sse_s": 1.5342614350374788, + "tpot_est_s": 0.006792701743242722, + "e2e_s": 1.7448351890780032 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " France\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 4.987368826055899, + "tpot_est_s": 0.006504017420323385, + "e2e_s": 5.188993366085924 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " France\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 3.323600427946076, + "tpot_est_s": 0.006287262099043977, + "e2e_s": 3.518505553016439 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " France\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 6.706171039026231, + "tpot_est_s": 0.006430647354723225, + "e2e_s": 6.9055211070226505 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 3.033789332024753, + "output_tok_s": 10.547864896948258, + "median_first_sse_s": 2.68975710705854, + "median_tpot_est_s": 0.011093486288201905, + "median_e2e_s": 3.033655181992799, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 2.68975710705854, + "tpot_est_s": 0.011093486288201905, + "e2e_s": 3.033655181992799 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 11.285970952943899, + "output_tok_s": 11.341514215629957, + "median_first_sse_s": 6.841305448964704, + "median_tpot_est_s": 0.011089284615140528, + "median_e2e_s": 7.180055449542124, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 5.502136196941137, + "tpot_est_s": 0.010759197614876734, + "e2e_s": 5.835671323002316 + }, + { + "id": "r2-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 8.18047470098827, + "tpot_est_s": 0.011095641132053588, + "e2e_s": 8.524439576081932 + }, + { + "id": "r2-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 2.6512444720137864, + "tpot_est_s": 0.011082928098227468, + "e2e_s": 2.994815243058838 + }, + { + "id": "r2-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue", + "text_equal": true, + "first_sse_s": 10.932211513980292, + "tpot_est_s": 0.011399817581649029, + "e2e_s": 11.285605859011412 + } + ] + } + ], + "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0", + "startup_timeout_s": 1800, + "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. Loading/warmup/correctness checks excluded.", + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216, + "server_config": { + "servers": [ + [ + "/home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python", + "-u", + "/home/gspark/workspace/codex-serving-matrix-201/container_bootstrap.py", + "/home/gspark/workspace/codex-serving-matrix-201/atom_server248.py", + "--model", + "Qwen/Qwen3-0.6B", + "--cache-dir", + "/home/gspark/workspace/codex-serving-matrix-201/atom-artifacts", + "--port", + "{port}", + "--batching", + "/home/gspark/workspace/codex-serving-matrix-201/atom_batching236.py", + "--http-module", + "/home/gspark/workspace/codex-serving-matrix-201/llmd214.py", + "--pd-module", + "/home/gspark/workspace/codex-serving-matrix-201/pd_batching238.py", + "--kv-blocks", + "3", + "--width", + "8192", + "--max-output", + "128", + "--worker-python", + "/home/gspark/workspace/codex-serving-matrix-201/worker_python234.sh", + "--engine-factory", + "long_width_variant:make_prefill" + ] + ], + "metadata": { + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216 + } + }, + "load_s": 369.7269653129624, + "references": { + "128:32": { + "tokens": [ + 9625, + 198, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303 + ], + "text": " France\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue" + }, + "8192:32": { + "tokens": [ + 12095, + 198, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303 + ], + "text": " Paris\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples. The blue" + } + }, + "warmup_and_checks_s": 29.055421822005883, + "metrics": [ + "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Qwen3-0.6B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Qwen3-0.6B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Qwen3-0.6B\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-0.6B\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-0.6B\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.5\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"5.0\"} 26\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"7.5\"} 34\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"10.0\"} 38\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-0.6B\"} 200.05287955782842\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-0.6B\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 21\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.025\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.05\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-0.6B\"} 0.36900967475958163\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-0.6B\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.0\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"2.5\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"5.0\"} 22\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"10.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"15.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"20.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"30.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-0.6B\"} 211.49217947537545\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-0.6B\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-0.6B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-0.6B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-0.6B\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-0.6B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-0.6B\",block_size=\"128\",enable_prefix_caching=\"True\"} 1\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Qwen3-0.6B\",side=\"remote\"} 42\nairm_pd_prefills_total{model_name=\"Qwen3-0.6B\",side=\"local\"} 0\n" + ], + "parity_passed": true +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-summary.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-summary.json new file mode 100644 index 00000000..3dd5d3b0 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen06b-summary.json @@ -0,0 +1,66 @@ +[ + { + "mode": "native", + "input_tokens": 128, + "concurrency": 1, + "output_tok_s": 141.93006079152747, + "median_first_sse_s": 0.013513133977539837, + "median_e2e_s": 0.2252974989823997 + }, + { + "mode": "native", + "input_tokens": 128, + "concurrency": 4, + "output_tok_s": 144.2299230912189, + "median_first_sse_s": 0.3467082384740934, + "median_e2e_s": 0.5559279784793034 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 1, + "output_tok_s": 22.237570437953742, + "median_first_sse_s": 1.0866091679781675, + "median_e2e_s": 1.4388405900681391 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 4, + "output_tok_s": 22.250476614941586, + "median_first_sse_s": 3.244740658032242, + "median_e2e_s": 3.5973375944886357 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 1, + "output_tok_s": 19.49478998302478, + "median_first_sse_s": 1.4414676959859207, + "median_e2e_s": 1.6413168660365045 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 4, + "output_tok_s": 19.02295644683716, + "median_first_sse_s": 4.066823305969592, + "median_e2e_s": 4.269759392482229 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 1, + "output_tok_s": 10.720150285988977, + "median_first_sse_s": 2.6405895240604877, + "median_e2e_s": 2.9849001180846244 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 4, + "output_tok_s": 10.581251020411687, + "median_first_sse_s": 7.281305430980865, + "median_e2e_s": 7.621745705546346 + } +] diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md index 6e0bdb0d..fbfed8d1 100644 --- a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md +++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md @@ -40,3 +40,24 @@ Initial PD run exited1 with TimeoutError("server startup") at the benchmark's180 Added --startup-timeout (default180 unchanged forGPU), positive-value validation, and recorded configured deadline inJSON. Red test1failed/2passed; fixed3passed in0.08s; Ruff clean after explicitcheck=False in the CLI negative test. RetriedPD with --startup-timeout1800 and output atom248-qwen06b-pd-b.json, retaining the original failure. Loading remains excluded from measured throughput. The owned Pod remains allocated with its original deadline; retry is live and no successful PD claim is made yet. Live retry observation: producer process still active after3m05s, averageCPU770% andRSS~12.7GiB, server waiting for producer readiness. This demonstrates a live CPU-active initialization, not successful completion or proof of the exact slow stage. Worker source loads weights toFP32 then constructs the compiled width8192engine before sendingready. Source also pads shorter prompts to compiledwidth; benchmark must not generalize fixed8192short-prompt costs to a width-dispatched implementation. No restart performed. + +Published candidatebb3ce4d as DraftPR252, stacked onPR251. Scoped sensitive scan clean. During live retry, /dev/shm contains only current896MiBslab; failed previous slab absent. Producer remains CPU-active, readiness pending. Full heterogeneous completion not claimed. + +## Qwen3-0.6B live PD completed + +Retry exited0:12measured PD batches pass token/text parity against nativeATOM. Producer readiness took369.727seconds (native5.762seconds), excluded from measurement. Metrics prove42remote/0local prefills including warmup/checks: no local fallback. Native+PD24measured batches total. + +|Mode|Input|Concurrency|tok/s|First SSE s|E2E s| +|---|---:|---:|---:|---:|---:| +|native|128|1|141.9301|0.0135|0.2253| +|native|128|4|144.2299|0.3467|0.5559| +|native|8192|1|22.2376|1.0866|1.4388| +|native|8192|4|22.2505|3.2447|3.5973| +|pd|128|1|19.4948|1.4415|1.6413| +|pd|128|4|19.0230|4.0668|4.2698| +|pd|8192|1|10.7202|2.6406|2.9849| +|pd|8192|4|10.5813|7.2813|7.6217| + +Current fixed-width8192research producer is slower than nativeATOM for both tested lengths. Short prompts are padded to8192; do not infer performance of a width-dispatched or native Furiosa engine. Preserve this actual result; correctness does not imply PD speedup. RawPD and summary copied locally as atom248-qwen06b-pd-b.json and atom248-qwen06b-summary.json. + +Started /tmp/run_atom248_remaining.py (copy in local analysis directory) for Qwen4B,KONI,Gemma native/PD sequentially on the same owned Pod, output32,input128/8192,c1/4,repeat3,startup1800. Exact commands and terminal codes in atom248-remaining.log; stops on any failed run or parity failure. Uses Gemma corrected artifactroot and KONIk1, existing verified factories. No new allocation or changes to other workloads. From 373a3942e0a8a3c70e3fdf1525a3056517eae811 Mon Sep 17 00:00:00 2001 From: gspark Date: Sat, 24 Oct 2026 02:22:01 +0900 Subject: [PATCH 3/4] exp: record Qwen4B and KONI RNGD-to-ATOM performance (#248) --- opt_prime/examples/pd/http_matrix/STATUS.md | 2 + .../atom248-koni-native-a.json | 9529 ++++++++++++++++ .../atom248-koni-pd-a.json | 9533 +++++++++++++++++ .../atom248-koni-summary.json | 58 + .../atom248-qwen4b-native-a.json | 9529 ++++++++++++++++ .../atom248-qwen4b-pd-a.json | 9533 +++++++++++++++++ .../atom248-qwen4b-summary.json | 50 + .../evidence248-heterogeneous/events.jsonl | 2 + ...2026-10-24-heterogeneous-performance248.md | 12 + 9 files changed, 38248 insertions(+) create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-native-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-pd-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-summary.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-native-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-pd-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-summary.json diff --git a/opt_prime/examples/pd/http_matrix/STATUS.md b/opt_prime/examples/pd/http_matrix/STATUS.md index 2fa16e7b..eb098092 100644 --- a/opt_prime/examples/pd/http_matrix/STATUS.md +++ b/opt_prime/examples/pd/http_matrix/STATUS.md @@ -43,3 +43,5 @@ BlockB reverse-model/mode repetition completed144additional batches with parity External-backend benchmark candidate:trusted argv config and explicit startup deadline. Real ATOM nativeQwen0.6B12batches pass parity; firstPDattempt timed out at180seconds before readiness, raw failure retained. Retry with1800seconds is live. No heterogeneous speedup claim.3harness tests pass; no core serving changes. Qwen0.6B actual RNGD-to-ATOM completed12PD batches with parity against nativeATOM;42remote/0localprefills. Readiness369.73s excluded from timing.8192input/c4 throughputnative22.25vsPD10.58tok/s. Current width8192producer is slower; shorter prompts also pad to8192. Other three model comparisons running. Failed180s startup record retained. + +Qwen3-4B and KONI8B RNGD-to-ATOM matched runs complete: each12native+12PD batches pass parity. Input8192/output32/c4: Qwen4B5.0853→5.4903tok/s;KONI3.3576→4.6973tok/s. Input128/c4:Qwen4B26.3399→7.4952;KONI14.6259→5.9530. Cold startup excluded (PD810.53s/432.86s). Both longer-model PD paths help on long input but regress on short input. Gemma native done,PD pending. diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-native-a.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-native-a.json new file mode 100644 index 00000000..d8390ebd --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-native-a.json @@ -0,0 +1,9529 @@ +{ + "passed": true, + "model": "KONI-Llama3.1-8B-Instruct-20241024", + "mode": "native", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 128000, + 2028, + 374, + 4092, + 2317, + 13, + 578, + 2579, + 3830, + 5727, + 41776, + 13, + 578, + 6437, + 3830, + 5727, + 6603, + 627, + 2028, + 374, + 4092, + 2317, + 13, + 578, + 2579, + 3830, + 5727, + 41776, + 13, + 578, + 6437, + 3830, + 5727, + 6603, + 627, + 2028, + 374, + 4092, + 2317, + 13, + 578, + 2579, + 3830, + 5727, + 41776, + 13, + 578, + 6437, + 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11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "warm-0-8192-32", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ], + "correctness_batches": [ + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-128-32-1-0", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-128-32-4-0", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-1", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-2", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-3", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-8192-32-1-0", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-8192-32-4-0", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-1", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-2", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-3", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ] + } + ], + "batches": [ + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 2.189042235026136, + "output_tok_s": 14.618265233981626, + "median_first_sse_s": 0.10254257102496922, + "median_tpot_est_s": 0.067300176514583, + "median_e2e_s": 2.1888480429770425, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 0.10254257102496922, + "tpot_est_s": 0.067300176514583, + "e2e_s": 2.1888480429770425 + } + ] + }, + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 8.753428473020904, + "output_tok_s": 14.622841826435328, + "median_first_sse_s": 3.3826632170239463, + "median_tpot_est_s": 0.06729312611165486, + "median_e2e_s": 5.467996570456307, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 2.2908159580547363, + "tpot_est_s": 0.06718615803026384, + "e2e_s": 4.373586856992915 + }, + { + "id": "r0-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 4.474510475993156, + "tpot_est_s": 0.06735147767504977, + "e2e_s": 6.5624062839196995 + }, + { + "id": "r0-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 0.10572258895263076, + "tpot_est_s": 0.06723477454825995, + "e2e_s": 2.1900005999486893 + }, + { + "id": "r0-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 6.664130042074248, + "tpot_est_s": 0.06738999541924004, + "e2e_s": 8.75321990007069 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 9.535770255024545, + "output_tok_s": 3.355785546861168, + "median_first_sse_s": 7.303306796005927, + "median_tpot_est_s": 0.07201021732462029, + "median_e2e_s": 9.535623533069156, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 7.303306796005927, + "tpot_est_s": 0.07201021732462029, + "e2e_s": 9.535623533069156 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 38.12219953094609, + "output_tok_s": 3.3576236831795256, + "median_first_sse_s": 21.602218537474982, + "median_tpot_est_s": 0.07196658842017754, + "median_e2e_s": 23.832052660465706, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 7.309453377034515, + "tpot_est_s": 0.07197131412929945, + "e2e_s": 9.540564115042798 + }, + { + "id": "r0-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 16.83777044294402, + "tpot_est_s": 0.07196387616286595, + "e2e_s": 19.068650603992864 + }, + { + "id": "r0-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 26.366666632005945, + "tpot_est_s": 0.07189638983653558, + "e2e_s": 28.595454716938548 + }, + { + "id": "r0-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. 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If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 4.479792976984754, + "tpot_est_s": 0.06721993796752705, + "e2e_s": 6.563611053978093 + }, + { + "id": "r1-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 6.664361811010167, + "tpot_est_s": 0.06732484883809041, + "e2e_s": 8.75143212499097 + }, + { + "id": "r1-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 0.10338407894596457, + "tpot_est_s": 0.0673659078085855, + "e2e_s": 2.1917272210121155 + }, + { + "id": "r1-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 2.2925835310015827, + "tpot_est_s": 0.06728871496799853, + "e2e_s": 4.378533695009537 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 2.1866427549393848, + "output_tok_s": 14.634306371132427, + "median_first_sse_s": 0.1018459980841726, + "median_tpot_est_s": 0.06724626887139053, + "median_e2e_s": 2.186480333097279, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 0.1018459980841726, + "tpot_est_s": 0.06724626887139053, + "e2e_s": 2.186480333097279 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 8.74654029100202, + "output_tok_s": 14.634357785063845, + "median_first_sse_s": 3.381895611528307, + "median_tpot_est_s": 0.0672566162576268, + "median_e2e_s": 5.467415022489149, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 0.10387870203703642, + "tpot_est_s": 0.06722383690442169, + "e2e_s": 2.187817646074109 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 2.2889355160295963, + "tpot_est_s": 0.06727949848338481, + "e2e_s": 4.374599969014525 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 6.661345583968796, + "tpot_est_s": 0.06724309158180991, + "e2e_s": 8.745881423004903 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 4.474855707027018, + "tpot_est_s": 0.0672701409334437, + "e2e_s": 6.560230075963773 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 9.527294219937176, + "output_tok_s": 3.358771048870895, + "median_first_sse_s": 7.299755539977923, + "median_tpot_est_s": 0.07185077867771109, + "median_e2e_s": 9.527129678986967, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. 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It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 7.308947309968062, + "tpot_est_s": 0.07201708251640442, + "e2e_s": 9.541476867976598 + }, + { + "id": "r2-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 26.36941576807294, + "tpot_est_s": 0.07187404861343244, + "e2e_s": 28.597511275089346 + } + ] + } + ], + "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0", + "startup_timeout_s": 1800, + "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. 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If yes, please let me know if you need any further assistance. \nIf not, just" + }, + "8192:32": { + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. 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9\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.75\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 13\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"5.0\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"7.5\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"10.0\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 436.45954173232894\nvllm:time_to_first_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 2.9205368813202384\nvllm:time_per_output_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"5.0\"} 13\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"10.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"15.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"20.0\"} 34\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"30.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 526.9961850532563\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n" + ], + "parity_passed": true +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-pd-a.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-pd-a.json new file mode 100644 index 00000000..2b140815 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-pd-a.json @@ -0,0 +1,9533 @@ +{ + "passed": true, + "model": "KONI-Llama3.1-8B-Instruct-20241024", + "mode": "pd", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 128000, + 2028, + 374, + 4092, + 2317, + 13, + 578, + 2579, + 3830, + 5727, + 41776, + 13, + 578, + 6437, + 3830, + 5727, + 6603, + 627, + 2028, + 374, + 4092, + 2317, + 13, + 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"warmups": [ + { + "id": "warm-0-128-32", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "warm-0-8192-32", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ], + "correctness_batches": [ + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-128-32-1-0", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-128-32-4-0", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-1", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-2", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + }, + { + "id": "check-128-32-4-3", + "endpoint": 0, + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-8192-32-1-0", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-8192-32-4-0", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-1", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-2", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-3", + "endpoint": 0, + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "tokens_equal": true + } + ] + } + ], + "batches": [ + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.38909105793573, + "output_tok_s": 5.937921563392079, + "median_first_sse_s": 3.309925716021098, + "median_tpot_est_s": 0.06706501870986915, + "median_e2e_s": 5.388941296027042, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.309925716021098, + "tpot_est_s": 0.06706501870986915, + "e2e_s": 5.388941296027042 + } + ] + }, + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 21.5017012989847, + "output_tok_s": 5.953017308730082, + "median_first_sse_s": 11.366825192060787, + "median_tpot_est_s": 0.06706601977273222, + "median_e2e_s": 13.447406426479574, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 8.67873188003432, + "tpot_est_s": 0.06704569435227782, + "e2e_s": 10.757148404954933 + }, + { + "id": "r0-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 14.054918504087254, + "tpot_est_s": 0.06718535302957941, + "e2e_s": 16.137664448004216 + }, + { + "id": "r0-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 19.421822155010886, + "tpot_est_s": 0.0670863451931866, + "e2e_s": 21.50149885599967 + }, + { + "id": "r0-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.286566955037415, + "tpot_est_s": 0.06691414673977922, + "e2e_s": 5.360905503970571 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.771967513021082, + "output_tok_s": 4.725362302531822, + "median_first_sse_s": 4.547305722022429, + "median_tpot_est_s": 0.07175844870958357, + "median_e2e_s": 6.77181763201952, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.547305722022429, + "tpot_est_s": 0.07175844870958357, + "e2e_s": 6.77181763201952 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 27.667035833001137, + "output_tok_s": 4.626444291777801, + "median_first_sse_s": 14.820643679006025, + "median_tpot_est_s": 0.07177160216322649, + "median_e2e_s": 17.04709006199846, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.589421449927613, + "tpot_est_s": 0.0717645448730177, + "e2e_s": 6.814122340991162 + }, + { + "id": "r0-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 25.442597949993797, + "tpot_est_s": 0.07173522212918126, + "e2e_s": 27.666389835998416 + }, + { + "id": "r0-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 18.231054347008467, + "tpot_est_s": 0.07177865945343528, + "e2e_s": 20.45619279006496 + }, + { + "id": "r0-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 11.410233011003584, + "tpot_est_s": 0.07186304267510892, + "e2e_s": 13.63798733393196 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.791054949047975, + "output_tok_s": 4.712080853429999, + "median_first_sse_s": 4.568467946024612, + "median_tpot_est_s": 0.07168988164349069, + "median_e2e_s": 6.790854276972823, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.568467946024612, + "tpot_est_s": 0.07168988164349069, + "e2e_s": 6.790854276972823 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 27.207363141002133, + "output_tok_s": 4.704608797869905, + "median_first_sse_s": 14.784360214485787, + "median_tpot_est_s": 0.071850270402783, + "median_e2e_s": 17.01171859697206, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.586560102063231, + "tpot_est_s": 0.07178186196758743, + "e2e_s": 6.811797823058441 + }, + { + "id": "r1-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 24.97780499397777, + "tpot_est_s": 0.07191516109742224, + "e2e_s": 27.20717498799786 + }, + { + "id": "r1-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 18.183884283993393, + "tpot_est_s": 0.07186434119414058, + "e2e_s": 20.41167886101175 + }, + { + "id": "r1-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 11.38483614497818, + "tpot_est_s": 0.0718361996114254, + "e2e_s": 13.611758332932368 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.4068339499644935, + "output_tok_s": 5.918435871367964, + "median_first_sse_s": 3.3214966490631923, + "median_tpot_est_s": 0.06726400064484726, + "median_e2e_s": 5.406680669053458, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.3214966490631923, + "tpot_est_s": 0.06726400064484726, + "e2e_s": 5.406680669053458 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 21.526452196063474, + "output_tok_s": 5.946172589620098, + "median_first_sse_s": 11.349469611537643, + "median_tpot_est_s": 0.06712050538795489, + "median_e2e_s": 13.429444116016384, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.2479430319508538, + "tpot_est_s": 0.06714450977667566, + "e2e_s": 5.329422835027799 + }, + { + "id": "r1-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 19.44103918399196, + "tpot_est_s": 0.06724736338571435, + "e2e_s": 21.525707448949106 + }, + { + "id": "r1-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 8.652322425041348, + "tpot_est_s": 0.06709540251552337, + "e2e_s": 10.732279903022572 + }, + { + "id": "r1-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 14.046616798033938, + "tpot_est_s": 0.06709650099923413, + "e2e_s": 16.126608329010196 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.3910391519311816, + "output_tok_s": 5.935775849176858, + "median_first_sse_s": 3.3143231120193377, + "median_tpot_est_s": 0.06698612771147201, + "median_e2e_s": 5.39089307107497, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.3143231120193377, + "tpot_est_s": 0.06698612771147201, + "e2e_s": 5.39089307107497 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 21.429160904954188, + "output_tok_s": 5.973169018036903, + "median_first_sse_s": 11.31757390149869, + "median_tpot_est_s": 0.06713701583888201, + "median_e2e_s": 13.400769380503334, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 13.989674094016664, + "tpot_est_s": 0.06713761545101841, + "e2e_s": 16.070940172998235 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 8.645473708980717, + "tpot_est_s": 0.06726209287186183, + "e2e_s": 10.730598588008434 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 3.2708366350270808, + "tpot_est_s": 0.06713641622674561, + "e2e_s": 5.352065538056195 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just", + "text_equal": true, + "first_sse_s": 19.350766863906756, + "tpot_est_s": 0.06702919148524562, + "e2e_s": 21.42867179994937 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.776354634086601, + "output_tok_s": 4.722303026915495, + "median_first_sse_s": 4.553356555989012, + "median_tpot_est_s": 0.07170412535478751, + "median_e2e_s": 6.776184441987425, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.553356555989012, + "tpot_est_s": 0.07170412535478751, + "e2e_s": 6.776184441987425 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 27.24984567402862, + "output_tok_s": 4.697274308675983, + "median_first_sse_s": 14.807121856545564, + "median_tpot_est_s": 0.07182771824074968, + "median_e2e_s": 17.033781122008804, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 4.6086105989525095, + "tpot_est_s": 0.07176147474186315, + "e2e_s": 6.833216315950267 + }, + { + "id": "r2-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 18.199610844021663, + "tpot_est_s": 0.07180944112910619, + "e2e_s": 20.425703519023955 + }, + { + "id": "r2-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 11.414632869069465, + "tpot_est_s": 0.0718459953523932, + "e2e_s": 13.641858724993654 + }, + { + "id": "r2-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the", + "text_equal": true, + "first_sse_s": 25.022104748990387, + "tpot_est_s": 0.07185022225753675, + "e2e_s": 27.249461638974026 + } + ] + } + ], + "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0", + "startup_timeout_s": 1800, + "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. Loading/warmup/correctness checks excluded.", + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216, + "server_config": { + "servers": [ + [ + "/home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python", + "-u", + "/home/gspark/workspace/codex-serving-matrix-201/container_bootstrap.py", + "/home/gspark/workspace/codex-serving-matrix-201/atom_server248.py", + "--model", + "KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024", + "--cache-dir", + "/home/gspark/workspace/codex-serving-matrix-201/atom-artifacts", + "--port", + "{port}", + "--batching", + "/home/gspark/workspace/codex-serving-matrix-201/atom_batching236.py", + "--http-module", + "/home/gspark/workspace/codex-serving-matrix-201/llmd214.py", + "--pd-module", + "/home/gspark/workspace/codex-serving-matrix-201/pd_batching238.py", + "--kv-blocks", + "1", + "--width", + "8192", + "--max-output", + "128", + "--worker-python", + "/home/gspark/workspace/codex-serving-matrix-201/worker_python234.sh", + "--engine-factory", + "koni_prefill:Prefill" + ] + ], + "metadata": { + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216 + } + }, + "load_s": 432.8649835359538, + "references": { + "128:32": { + "tokens": [ + 578, + 6864, + 315, + 9822, + 374, + 12366, + 13, + 720, + 3957, + 420, + 2038, + 11190, + 30, + 1442, + 10035, + 11, + 4587, + 1095, + 757, + 1440, + 422, + 499, + 1205, + 904, + 4726, + 13291, + 13, + 720, + 2746, + 539, + 11, + 1120 + ], + "text": " The capital of France is Paris. \nIs this information helpful? If yes, please let me know if you need any further assistance. \nIf not, just" + }, + "8192:32": { + "tokens": [ + 12366, + 13, + 720, + 70869, + 25, + 12366, + 374, + 279, + 6864, + 3363, + 315, + 9822, + 11, + 7559, + 304, + 279, + 18671, + 961, + 315, + 279, + 3224, + 13, + 1102, + 374, + 3967, + 369, + 1202, + 27373, + 61024, + 1778, + 439, + 279 + ], + "text": " Paris. \nExplanation: Paris is the capital city of France, located in the northern part of the country. It is known for its iconic landmarks such as the" + } + }, + "warmup_and_checks_s": 73.86173666000832, + "metrics": [ + "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds 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458.0435710591264\nvllm:time_to_first_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 2.914744678159965\nvllm:time_per_output_token_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"2.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"5.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"10.0\"} 18\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"15.0\"} 26\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"20.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"30.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 548.4006560820853\nvllm:e2e_request_latency_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",block_size=\"128\",enable_prefix_caching=\"True\"} 1\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",side=\"remote\"} 42\nairm_pd_prefills_total{model_name=\"KONI-Llama3.1-8B-Instruct-20241024\",side=\"local\"} 0\n" + ], + "parity_passed": true +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-summary.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-summary.json new file mode 100644 index 00000000..f62ba493 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-koni-summary.json @@ -0,0 +1,58 @@ +[ + { + "mode": "native", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 14.618488908474202, + "e2e_s": 2.1888480429770425 + }, + { + "mode": "native", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 14.625890681706176, + "e2e_s": 5.467996570456307 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 3.357969479018503, + "e2e_s": 9.529372807010077 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 3.3576236831795256, + "e2e_s": 23.832052660465706 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 5.935775849176858, + "e2e_s": 5.39089307107497 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 5.953017308730082, + "e2e_s": 13.429444116016384 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 4.722303026915495, + "e2e_s": 6.776184441987425 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 4.697274308675983, + "e2e_s": 17.033781122008804 + } +] diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-native-a.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-native-a.json new file mode 100644 index 00000000..75832caf --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-native-a.json @@ -0,0 +1,9529 @@ +{ + "passed": true, + "model": "Qwen3-4B", + "mode": "native", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 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3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 198, + 14582, + 25, + 3555, + 374, + 279, + 6722, + 315, + 9625, + 5267, + 16141, + 25 + ] + }, + "warmups": [ + { + "id": "warm-0-128-32", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "warm-0-8192-32", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ], + "correctness_batches": [ + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-128-32-1-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-128-32-4-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-1", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-2", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-3", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-8192-32-1-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-8192-32-4-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-1", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-2", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-3", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + } + ], + "batches": [ + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.213416169048287, + "output_tok_s": 26.371825937591066, + "median_first_sse_s": 0.06453614693600684, + "median_tpot_est_s": 0.03705477164757828, + "median_e2e_s": 1.2132340680109337, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06453614693600684, + "tpot_est_s": 0.03705477164757828, + "e2e_s": 1.2132340680109337 + } + ] + }, + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 4.852803061017767, + "output_tok_s": 26.376508255242253, + "median_first_sse_s": 1.8822612579679117, + "median_tpot_est_s": 0.0370983969525344, + "median_e2e_s": 3.0327410720055923, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.7017785659991205, + "tpot_est_s": 0.03710970996818956, + "e2e_s": 4.852179575012997 + }, + { + "id": "r0-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 1.2765399219933897, + "tpot_est_s": 0.037087083936879234, + "e2e_s": 2.426239524036646 + }, + { + "id": "r0-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06476684904191643, + "tpot_est_s": 0.03706081686992078, + "e2e_s": 1.2136521720094606 + }, + { + "id": "r0-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 2.4879825939424336, + "tpot_est_s": 0.037137420194584034, + "e2e_s": 3.6392426199745387 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.296314295032062, + "output_tok_s": 5.0823384126883155, + "median_first_sse_s": 4.968130536028184, + "median_tpot_est_s": 0.04284036893307442, + "median_e2e_s": 6.296181972953491, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.968130536028184, + "tpot_est_s": 0.04284036893307442, + "e2e_s": 6.296181972953491 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 25.167794405017048, + "output_tok_s": 5.085864813584299, + "median_first_sse_s": 14.416826851957012, + "median_tpot_est_s": 0.04271678974281155, + "median_e2e_s": 15.743958546954673, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 17.565695649944246, + "tpot_est_s": 0.04265723241898682, + "e2e_s": 18.888069854932837 + }, + { + "id": "r0-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.973565525026061, + "tpot_est_s": 0.042776347066636286, + "e2e_s": 6.2996322840917856 + }, + { + "id": "r0-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.267958053969778, + "tpot_est_s": 0.04296416725828162, + "e2e_s": 12.599847238976508 + }, + { + "id": "r0-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 23.856513883918524, + "tpot_est_s": 0.04228700796938351, + "e2e_s": 25.167411130969413 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.288122335099615, + "output_tok_s": 5.088959516798756, + "median_first_sse_s": 4.968080218997784, + "median_tpot_est_s": 0.042577450775781706, + "median_e2e_s": 6.287981193047017, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.968080218997784, + "tpot_est_s": 0.042577450775781706, + "e2e_s": 6.287981193047017 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 25.170623405021615, + "output_tok_s": 5.085293198358513, + "median_first_sse_s": 14.411285778449383, + "median_tpot_est_s": 0.04271067566268386, + "median_e2e_s": 15.736293819500133, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 17.556203510961495, + "tpot_est_s": 0.042771756615219335, + "e2e_s": 18.882127966033295 + }, + { + "id": "r1-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 23.847010919009335, + "tpot_est_s": 0.042691993996531015, + "e2e_s": 25.170462732901797 + }, + { + "id": "r1-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.26636804593727, + "tpot_est_s": 0.042712633129990386, + "e2e_s": 12.590459672966972 + }, + { + "id": "r1-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.974895369959995, + "tpot_est_s": 0.04270871819537734, + "e2e_s": 6.298865634016693 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.210708333994262, + "output_tok_s": 26.430808396625494, + "median_first_sse_s": 0.06415817304514349, + "median_tpot_est_s": 0.0369800899981431, + "median_e2e_s": 1.2105409629875794, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06415817304514349, + "tpot_est_s": 0.0369800899981431, + "e2e_s": 1.2105409629875794 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 4.859552640002221, + "output_tok_s": 26.339873128721063, + "median_first_sse_s": 1.8854154655127786, + "median_tpot_est_s": 0.0371519306791015, + "median_e2e_s": 3.0385494850343093, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06565357802901417, + "tpot_est_s": 0.0370803693226809, + "e2e_s": 1.215145027032122 + }, + { + "id": "r1-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.7084882840281352, + "tpot_est_s": 0.037108295097676734, + "e2e_s": 4.858845432056114 + }, + { + "id": "r1-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 2.493361108005047, + "tpot_est_s": 0.03720017693441121, + "e2e_s": 3.6465665929717943 + }, + { + "id": "r1-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 1.2774698230205104, + "tpot_est_s": 0.03719556626052626, + "e2e_s": 2.4305323770968243 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.2151768079493195, + "output_tok_s": 26.333616466892448, + "median_first_sse_s": 0.06395557092037052, + "median_tpot_est_s": 0.03712982854865972, + "median_e2e_s": 1.2149802559288219, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06395557092037052, + "tpot_est_s": 0.03712982854865972, + "e2e_s": 1.2149802559288219 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 4.869658071896993, + "output_tok_s": 26.2852130704399, + "median_first_sse_s": 1.8888076295261271, + "median_tpot_est_s": 0.037228455483507845, + "median_e2e_s": 3.043355388508644, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 0.06451145699247718, + "tpot_est_s": 0.03722608367717194, + "e2e_s": 1.2185200509848073 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 2.496783962007612, + "tpot_est_s": 0.037272232840017926, + "e2e_s": 3.6522231800481677 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 1.2808312970446423, + "tpot_est_s": 0.03721471935240252, + "e2e_s": 2.43448759696912 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.7149379400070757, + "tpot_est_s": 0.037230827289843754, + "e2e_s": 4.869093585992232 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 6.292998133925721, + "output_tok_s": 5.08501660400742, + "median_first_sse_s": 4.971413867082447, + "median_tpot_est_s": 0.04262639503295143, + "median_e2e_s": 6.292832113103941, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.971413867082447, + "tpot_est_s": 0.04262639503295143, + "e2e_s": 6.292832113103941 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 25.178712520049885, + "output_tok_s": 5.083659456299571, + "median_first_sse_s": 14.413789580517914, + "median_tpot_est_s": 0.04275945574232948, + "median_e2e_s": 15.739960638980847, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.266487421002239, + "tpot_est_s": 0.04283791128921533, + "e2e_s": 12.594462670967914 + }, + { + "id": "r2-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 23.852129850070924, + "tpot_est_s": 0.04277202299976301, + "e2e_s": 25.178062563063577 + }, + { + "id": "r2-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 17.56109174003359, + "tpot_est_s": 0.042721511837425494, + "e2e_s": 18.88545860699378 + }, + { + "id": "r2-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.974650949938223, + "tpot_est_s": 0.042746888484895953, + "e2e_s": 6.299804492969997 + } + ] + } + ], + "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0", + "startup_timeout_s": 1800, + "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. Loading/warmup/correctness checks excluded.", + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 0, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216, + "server_config": { + "servers": [ + [ + "/home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python", + "-u", + "/home/gspark/workspace/codex-serving-matrix-201/container_bootstrap.py", + "/home/gspark/workspace/codex-serving-matrix-201/atom_server248.py", + "--model", + "Qwen/Qwen3-4B", + "--cache-dir", + "/home/gspark/workspace/codex-serving-matrix-201/atom-artifacts", + "--port", + "{port}", + "--batching", + "/home/gspark/workspace/codex-serving-matrix-201/atom_batching236.py", + "--http-module", + "/home/gspark/workspace/codex-serving-matrix-201/llmd214.py", + "--pd-module", + "/home/gspark/workspace/codex-serving-matrix-201/pd_batching238.py", + "--kv-blocks", + "3", + "--width", + "8192", + "--max-output", + "128" + ] + ], + "metadata": { + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 0, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216 + } + }, + "load_s": 11.528773405007087, + "references": { + "128:32": { + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at" + }, + "8192:32": { + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at" + } + }, + "warmup_and_checks_s": 45.02003912604414, + "metrics": [ + "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Qwen3-4B\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-4B\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-4B\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.08\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 9\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"7.5\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 30\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-4B\"} 285.7216112901224\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-4B\"} 1.675375008666437\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.5\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.0\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 13\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 21\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"15.0\"} 34\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"20.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"30.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-4B\"} 337.65823655878194\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-4B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-4B\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-4B\",block_size=\"128\",enable_prefix_caching=\"True\"} 1\n" + ], + "parity_passed": true +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-pd-a.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-pd-a.json new file mode 100644 index 00000000..16dc9083 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-pd-a.json @@ -0,0 +1,9533 @@ +{ + "passed": true, + "model": "Qwen3-4B", + "mode": "pd", + "dtype": "bfloat16", + "torch": "2.11.0+cpu", + "transformers": "5.8.1", + "replicas": 1, + "cpu_threads_per_server": 4, + "prompts": { + "128": [ + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, 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576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 2266, + 13, + 576, + 2518, + 3745, + 5610, + 40676, + 13, + 576, + 6303, + 3745, + 5610, + 6467, + 624, + 1986, + 374, + 4004, + 198, + 14582, + 25, + 3555, + 374, + 279, + 6722, + 315, + 9625, + 5267, + 16141, + 25 + ] + }, + "warmups": [ + { + "id": "warm-0-128-32", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "warm-0-8192-32", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ], + "correctness_batches": [ + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-128-32-1-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-128-32-4-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-1", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-2", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-128-32-4-3", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 1, + "rows": [ + { + "id": "check-8192-32-1-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + }, + { + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "rows": [ + { + "id": "check-8192-32-4-0", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-1", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-2", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-3", + "endpoint": 0, + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "tokens_equal": true + } + ] + } + ], + "batches": [ + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 4.29774594202172, + "output_tok_s": 7.445763530858399, + "median_first_sse_s": 3.146953364019282, + "median_tpot_est_s": 0.03711665922685737, + "median_e2e_s": 4.2975698000518605, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.146953364019282, + "tpot_est_s": 0.03711665922685737, + "e2e_s": 4.2975698000518605 + } + ] + }, + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 17.077518068952486, + "output_tok_s": 7.495234347472798, + "median_first_sse_s": 9.509424406511243, + "median_tpot_est_s": 0.03711307301719283, + "median_e2e_s": 10.659543810004834, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 7.371769164106809, + "tpot_est_s": 0.037098406126812825, + "e2e_s": 8.521819754038006 + }, + { + "id": "r0-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.1316037620417774, + "tpot_est_s": 0.037136900933429355, + "e2e_s": 4.2828476909780875 + }, + { + "id": "r0-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 15.926055789925158, + "tpot_est_s": 0.0371233003229023, + "e2e_s": 17.07687809993513 + }, + { + "id": "r0-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.647079648915678, + "tpot_est_s": 0.03710284571148335, + "e2e_s": 12.797267865971662 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.850729540921748, + "output_tok_s": 5.469403392548306, + "median_first_sse_s": 4.540284454007633, + "median_tpot_est_s": 0.04226665367417398, + "median_e2e_s": 5.850550717907026, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.540284454007633, + "tpot_est_s": 0.04226665367417398, + "e2e_s": 5.850550717907026 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 23.475091358995996, + "output_tok_s": 5.45258793853207, + "median_first_sse_s": 13.425524136517197, + "median_tpot_est_s": 0.04268467083813683, + "median_e2e_s": 14.749242957506794, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 10.490344834048301, + "tpot_est_s": 0.04265013690512147, + "e2e_s": 11.812499078107066 + }, + { + "id": "r0-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 16.360703438986093, + "tpot_est_s": 0.042751077352271925, + "e2e_s": 17.685986836906523 + }, + { + "id": "r0-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.603961855988018, + "tpot_est_s": 0.04260975177279643, + "e2e_s": 5.924864160944708 + }, + { + "id": "r0-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 22.150245064054616, + "tpot_est_s": 0.04271920477115219, + "e2e_s": 23.474540411960334 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.678877111990005, + "output_tok_s": 5.634916792342155, + "median_first_sse_s": 4.355931215919554, + "median_tpot_est_s": 0.042670698517242506, + "median_e2e_s": 5.678722869954072, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.355931215919554, + "tpot_est_s": 0.042670698517242506, + "e2e_s": 5.678722869954072 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 23.31389832799323, + "output_tok_s": 5.490287304131765, + "median_first_sse_s": 13.246086586557794, + "median_tpot_est_s": 0.04252774083814133, + "median_e2e_s": 14.564446552540176, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 10.340867737075314, + "tpot_est_s": 0.042648000837934595, + "e2e_s": 11.662955763051286 + }, + { + "id": "r1-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 16.151305436040275, + "tpot_est_s": 0.04240748083834807, + "e2e_s": 17.465937342029065 + }, + { + "id": "r1-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 21.986193950055167, + "tpot_est_s": 0.04281496461255536, + "e2e_s": 23.313457853044383 + }, + { + "id": "r1-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.519232189981267, + "tpot_est_s": 0.04238094399953561, + "e2e_s": 5.833041453966871 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 4.237617821083404, + "output_tok_s": 7.551412456496317, + "median_first_sse_s": 3.0850161019479856, + "median_tpot_est_s": 0.03717445151790256, + "median_e2e_s": 4.237424099002965, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.0850161019479856, + "tpot_est_s": 0.03717445151790256, + "e2e_s": 4.237424099002965 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 17.383791868924163, + "output_tok_s": 7.363180655011004, + "median_first_sse_s": 9.633522957505193, + "median_tpot_est_s": 0.03724837703292348, + "median_e2e_s": 10.78822264552582, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.1055604870198295, + "tpot_est_s": 0.03720418767883412, + "e2e_s": 4.258890305063687 + }, + { + "id": "r1-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 16.225537763908505, + "tpot_est_s": 0.03734207538993008, + "e2e_s": 17.383142100996338 + }, + { + "id": "r1-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.899985246011056, + "tpot_est_s": 0.03722807561467972, + "e2e_s": 13.054055590066127 + }, + { + "id": "r1-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 7.367060668999329, + "tpot_est_s": 0.03726867845116724, + "e2e_s": 8.522389700985514 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 4.283906926983036, + "output_tok_s": 7.469816815683288, + "median_first_sse_s": 3.1328047439455986, + "median_tpot_est_s": 0.0371267587742618, + "median_e2e_s": 4.2837342659477144, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.1328047439455986, + "tpot_est_s": 0.0371267587742618, + "e2e_s": 4.2837342659477144 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 16.913559213047847, + "output_tok_s": 7.5678926231715495, + "median_first_sse_s": 9.454643925011624, + "median_tpot_est_s": 0.03722220061219207, + "median_e2e_s": 10.608532143989578, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 3.1177031160332263, + "tpot_est_s": 0.037083527580234074, + "e2e_s": 4.2672924710204825 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 15.757904791040346, + "tpot_est_s": 0.0372722351270157, + "e2e_s": 16.913344079977833 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 7.332786679035053, + "tpot_est_s": 0.03718614661239929, + "e2e_s": 8.48555722401943 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 11.576501170988195, + "tpot_est_s": 0.037258254611984856, + "e2e_s": 12.731507063959725 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 5.91328890400473, + "output_tok_s": 5.4115400954498, + "median_first_sse_s": 4.5871634759241715, + "median_tpot_est_s": 0.04277278567784496, + "median_e2e_s": 5.913119831937365, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.5871634759241715, + "tpot_est_s": 0.04277278567784496, + "e2e_s": 5.913119831937365 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 23.15381483605597, + "output_tok_s": 5.528246680139883, + "median_first_sse_s": 13.11163710145047, + "median_tpot_est_s": 0.04275666755030773, + "median_e2e_s": 14.437387344019953, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 10.252728285966441, + "tpot_est_s": 0.042790425550793446, + "e2e_s": 11.579231478041038 + }, + { + "id": "r2-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 21.82920672709588, + "tpot_est_s": 0.04272246825689029, + "e2e_s": 23.15360324305948 + }, + { + "id": "r2-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 4.4838149739662185, + "tpot_est_s": 0.04277148693724866, + "e2e_s": 5.809731069020927 + }, + { + "id": "r2-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at", + "text_equal": true, + "first_sse_s": 15.970545916934498, + "tpot_est_s": 0.0427418481633668, + "e2e_s": 17.29554320999887 + } + ] + } + ], + "harness_sha256": "cfd4821e2a2112f3fcf7f242f3e76efe47134640e4bf22b3b63fb808be6148f0", + "startup_timeout_s": 1800, + "timing_scope": "Client first SSE event, full response e2e and estimated TPOT=(e2e-first_event)/(output_tokens-1). SSE can coalesce tokens; this is not per-token kernel timing. Loading/warmup/correctness checks excluded.", + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216, + "server_config": { + "servers": [ + [ + "/home/gspark/workspace/01_pd_disag/experiments/native_pd_2026_4/p2p_dma/devkit/torch-venv/bin/python", + "-u", + "/home/gspark/workspace/codex-serving-matrix-201/container_bootstrap.py", + "/home/gspark/workspace/codex-serving-matrix-201/atom_server248.py", + "--model", + "Qwen/Qwen3-4B", + "--cache-dir", + "/home/gspark/workspace/codex-serving-matrix-201/atom-artifacts", + "--port", + "{port}", + "--batching", + "/home/gspark/workspace/codex-serving-matrix-201/atom_batching236.py", + "--http-module", + "/home/gspark/workspace/codex-serving-matrix-201/llmd214.py", + "--pd-module", + "/home/gspark/workspace/codex-serving-matrix-201/pd_batching238.py", + "--kv-blocks", + "3", + "--width", + "8192", + "--max-output", + "128", + "--worker-python", + "/home/gspark/workspace/codex-serving-matrix-201/worker_python234.sh", + "--engine-factory", + "long_width_variant:make_prefill" + ] + ], + "metadata": { + "physical_gpus": 0, + "physical_atom": 1, + "physical_rngd": 1, + "slots_per_decoder": 1, + "producer_cache": false, + "decoder_cache": false, + "producer_chunk": null, + "local_chunk": null, + "context": 9216 + } + }, + "load_s": 810.5300242629601, + "references": { + "128:32": { + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at" + }, + "8192:32": { + "tokens": [ + 576, + 6722, + 315, + 9625, + 374, + 12095, + 624, + 32313, + 11, + 773, + 279, + 1196, + 4588, + 264, + 3405, + 911, + 279, + 6722, + 315, + 9625, + 11, + 323, + 358, + 3897, + 279, + 4226, + 12095, + 13, + 1988, + 3330, + 1182, + 518 + ], + "text": " The capital of France is Paris.\nOkay, so the user asked a question about the capital of France, and I provided the answer Paris. But looking back at" + } + }, + "warmup_and_checks_s": 61.74552664195653, + "metrics": [ + "# HELP vllm:num_requests_running Requests executing on the stage.\n# TYPE vllm:num_requests_running gauge\nvllm:num_requests_running{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:num_requests_waiting Requests queued for the stage owner.\n# TYPE vllm:num_requests_waiting gauge\nvllm:num_requests_waiting{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:kv_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:kv_cache_usage_perc gauge\nvllm:kv_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:gpu_cache_usage_perc Occupied fraction of the stage's KV lanes (1 = full).\n# TYPE vllm:gpu_cache_usage_perc gauge\nvllm:gpu_cache_usage_perc{model_name=\"Qwen3-4B\"} 0.0\n# HELP vllm:request_success_total Finished requests by finish reason.\n# TYPE vllm:request_success_total counter\nvllm:request_success_total{model_name=\"Qwen3-4B\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"Qwen3-4B\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"Qwen3-4B\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 17\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"7.5\"} 22\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 22\nvllm:time_to_first_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"Qwen3-4B\"} 403.41920946293976\nvllm:time_to_first_token_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"Qwen3-4B\"} 1.6756548447451824\nvllm:time_per_output_token_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"2.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"5.0\"} 9\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"10.0\"} 22\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"15.0\"} 30\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"20.0\"} 38\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"30.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"Qwen3-4B\"} 455.3645096500404\nvllm:e2e_request_latency_seconds_count{model_name=\"Qwen3-4B\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"Qwen3-4B\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"Qwen3-4B\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"Qwen3-4B\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"Qwen3-4B\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"Qwen3-4B\",block_size=\"128\",enable_prefix_caching=\"True\"} 1\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"remote\"} 42\nairm_pd_prefills_total{model_name=\"Qwen3-4B\",side=\"local\"} 0\n" + ], + "parity_passed": true +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-summary.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-summary.json new file mode 100644 index 00000000..d0827cce --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-qwen4b-summary.json @@ -0,0 +1,50 @@ +[ + { + "mode": "native", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 26.371825937591066 + }, + { + "mode": "native", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 26.339873128721063 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 5.08501660400742 + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 5.085293198358513 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 7.469816815683288 + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 7.495234347472798 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 5.469403392548306 + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 5.490287304131765 + } +] diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl index 404a81ac..9a933a31 100644 --- a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl @@ -2,3 +2,5 @@ {"id": "7987a83b7679464f96e245634ff1f5e3", "ts": "2026-10-24T01:25:53+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Actual ATOM native Qwen3-0.6B fixed-output benchmark completed12batches with parity; launched live RNGD-to-ATOM against identical native reference. GPU long128 queue remains active separately.", "next": "Inspect heterogeneous PD output parity and performance; expand supported model artifacts and retain failures.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"model": "Qwen3-0.6B", "backend": "atom", "output_tokens": "32", "input_tokens": "128,8192", "concurrency": "1,4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen06b-native-a.json", "size": 139194, "sha256": "a17f3e37ab3cec64"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} {"id": "a06bf63de3974c3a98104fbd63867f33", "ts": "2026-10-24T01:30:43+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Heterogeneous PD attempt failed before measurement at outer180s startup deadline, versus worker1800s bound. Preserved failure; added explicit positive --startup-timeout with3tests and retried under1800s. GPU Qwen0.6B native output128/c8 passed; PD ongoing.", "next": "Track same heterogeneous retry and inspect initialization progress; finish GPU extended matrix; no PD speedup claim until parity/timing available.", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {"model": "Qwen3-0.6B", "route": "RNGD-ATOM", "startup_timeout_s": "1800"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen06b-pd-a.json", "size": 98674, "sha256": "2fff4ddcbfbb8716"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} {"id": "2651ff872dd84956ac5ebf56212b0f34", "ts": "2026-10-24T01:33:41+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Qwen3-0.6B output128 extension:54batches all parity pass;8192input/c8 native65.74,PD74.66,DP2120.65tok_s. Qwen4B native started. RNGD retry confirmed live CPU-active initialization at3min; readiness pending.", "next": "Continue both existing jobs; collect Qwen4B long-output results and RNGD readiness without duplicate launches.", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "74.6554639496"}, "params": {"model": "Qwen3-0.6B", "output_tokens": "128", "input_tokens": "8192", "concurrency": "8", "mode": "pd"}, "artifacts": [{"path": "analysis/serving-matrix-201/perf248-qwen06b-long128-summary.json", "size": 1939, "sha256": "cc8f91ae525cc8da"}], "experiments": ["experiments/2026-10-24-matched-pd-performance248.md", "experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} +{"id": "0f82a718778141e5bc5806e2f0113b30", "ts": "2026-10-24T02:01:29+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Qwen4B actual RNGD-to-ATOM12PD batches pass native parity;startup810.53s excluded.8192/c4 native5.09vsPD5.49tok_s;128/c4 native26.34vsPD7.50. Queue moved toKONI; finalGPU Gemma DP2 still live.", "next": "", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "5.4902873041"}, "params": {"model": "Qwen3-4B", "route": "RNGD-ATOM", "input_tokens": "8192", "output_tokens": "32", "concurrency": "4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen4b-summary.json", "size": 864, "sha256": "e77b5b228fe5e49a"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"} +{"id": "5994afa933e342c1b25360ef6011fb4e", "ts": "2026-10-24T02:19:38+09:00", "task": "Matched PD performance (aicomp #248)", "result": "KONI8B actual RNGD-ATOM24native/PD batches pass parity,42remote/0local.8192/c4 native3.36vsPD4.70tok_s(+39.9%);128/c4 native14.63vsPD5.95. Gemma native done,PD live.", "next": "Finish Gemma; publish full four-model heterogeneous results", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "4.6972743087"}, "params": {"model": "KONI-8B", "input_tokens": "8192", "output_tokens": "32", "concurrency": "4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-koni-summary.json", "size": 1125, "sha256": "725e157d79343daa"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"} diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md index fbfed8d1..a8fd9645 100644 --- a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md +++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md @@ -61,3 +61,15 @@ Retry exited0:12measured PD batches pass token/text parity against nativeATOM. P Current fixed-width8192research producer is slower than nativeATOM for both tested lengths. Short prompts are padded to8192; do not infer performance of a width-dispatched or native Furiosa engine. Preserve this actual result; correctness does not imply PD speedup. RawPD and summary copied locally as atom248-qwen06b-pd-b.json and atom248-qwen06b-summary.json. Started /tmp/run_atom248_remaining.py (copy in local analysis directory) for Qwen4B,KONI,Gemma native/PD sequentially on the same owned Pod, output32,input128/8192,c1/4,repeat3,startup1800. Exact commands and terminal codes in atom248-remaining.log; stops on any failed run or parity failure. Uses Gemma corrected artifactroot and KONIk1, existing verified factories. No new allocation or changes to other workloads. + +Qwen3-4B nativeATOM completed12batches with parity, startup11.5288seconds. Atoutput32/c4,input128 throughput26.3399tok/s,input8192=5.0853tok/s. Live RNGD-to-ATOM Qwen4producer initialization now running; native does not establish cross-device parity. + +Source-level B1 admission audit:AtomBatcher._step_pd submits remote prefill only while self.free and self.waiting. A single occupied decode lane prevents next-request producer submission until decode finishes. This rules out cross-request producer/decode overlap in this measured B1 configuration; it does not by itself quantify the speedup available. Issue253 records bounded independent prefill ownership and cancellation/failure tests required before changing this behavior. Distinct from within-request chunk-copy issue47; fixedwidth compute cost remains. + +Qwen3-4B native/PD each12batches completed with parity. PD startup810.530seconds excluded from measurement. Output32/c4,input128 native26.3399vsPD7.4952tok/s; input8192 native5.0853vsPD5.4903tok/s. Fixed-width8192PD is slower for short inputs and modestly faster in this long-input sample; no repetition/overlap claim yet. Summary atom248-qwen4b-summary.json; queue advanced toKONI native. + +## KONI-8B matched result + +Native and actual RNGD→ATOM each completed 12 batches with token/text parity; PD reports 42 remote and 0 local prefills. Native startup 17.332 s, PD startup 432.865 s, both excluded from timed requests. Same fixed width8192, B1, output32, three repeats as above. Commands in `analysis/serving-matrix-201/atom248-remaining.log`; raw `atom248-koni-{native,pd}-a.json` and summary `atom248-koni-summary.json`. + +At input8192/c4: native 3.3576 tok/s, PD 4.6973 tok/s (+39.9%); median request e2e 23.832 -> 17.034 s. At input128/c4: native 14.6259 tok/s, PD 5.9530 tok/s. Long-prompt benefit and short-prompt regression are from one workload block, not a universal result. Gemma native completed and PD is running. From b247d012c2681148f32a9ebabb7df664ad11d1be Mon Sep 17 00:00:00 2001 From: gspark Date: Sat, 24 Oct 2026 02:42:19 +0900 Subject: [PATCH 4/4] exp: retain Gemma forced-output mismatch and complete NPU matrix (#248) --- .../examples/pd/http_matrix/.gitattributes | 1 + opt_prime/examples/pd/http_matrix/STATUS.md | 2 + .../atom248-four-model-summary.json | 111 + .../atom248-gemma-eos-audit.json | 90 + .../atom248-gemma-native-a.json | 9529 ++++++++++++++++ .../atom248-gemma-pd-a.json | 9533 +++++++++++++++++ .../atom248-gemma-pd-a.log | 15 + .../atom248-gemma-summary.json | 66 + .../evidence248-heterogeneous/events.jsonl | 1 + ...2026-10-24-heterogeneous-performance248.md | 23 + 10 files changed, 19371 insertions(+) create mode 100644 opt_prime/examples/pd/http_matrix/.gitattributes create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-four-model-summary.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-eos-audit.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-native-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-pd-a.json create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-pd-a.log create mode 100644 opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-summary.json diff --git a/opt_prime/examples/pd/http_matrix/.gitattributes b/opt_prime/examples/pd/http_matrix/.gitattributes new file mode 100644 index 00000000..91d78b00 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/.gitattributes @@ -0,0 +1 @@ +evidence*/*.json linguist-generated=true diff --git a/opt_prime/examples/pd/http_matrix/STATUS.md b/opt_prime/examples/pd/http_matrix/STATUS.md index eb098092..d0f82725 100644 --- a/opt_prime/examples/pd/http_matrix/STATUS.md +++ b/opt_prime/examples/pd/http_matrix/STATUS.md @@ -45,3 +45,5 @@ External-backend benchmark candidate:trusted argv config and explicit startup de Qwen0.6B actual RNGD-to-ATOM completed12PD batches with parity against nativeATOM;42remote/0localprefills. Readiness369.73s excluded from timing.8192input/c4 throughputnative22.25vsPD10.58tok/s. Current width8192producer is slower; shorter prompts also pad to8192. Other three model comparisons running. Failed180s startup record retained. Qwen3-4B and KONI8B RNGD-to-ATOM matched runs complete: each12native+12PD batches pass parity. Input8192/output32/c4: Qwen4B5.0853→5.4903tok/s;KONI3.3576→4.6973tok/s. Input128/c4:Qwen4B26.3399→7.4952;KONI14.6259→5.9530. Cold startup excluded (PD810.53s/432.86s). Both longer-model PD paths help on long input but regress on short input. Gemma native done,PD pending. + +Four-model heterogeneous matrix completed96batches. Gemma short forced-output parity fails afterEOS: firstEOS index2, divergence index5. Offline through-EOS prefixes match; not a new normal-EOS HTTP run. Long Gemma parity passes but PD14.94vsnative27.75tok/s. Each PD path42remote/0local. Raw failure retained; no all-model parity claim. Owned benchmark Pod deleted and NotFound verified. Earlier pre-EOS numerical issue#233 remains open. diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-four-model-summary.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-four-model-summary.json new file mode 100644 index 00000000..c33ab413 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-four-model-summary.json @@ -0,0 +1,111 @@ +{ + "measured_batches": 96, + "all_workflows_completed": true, + "full_parity": false, + "rows": [ + { + "model": "qwen06b", + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 144.2299230912189, + "native_text_parity": true, + "pd_tok_s": 19.02295644683716, + "pd_text_parity": true, + "pd_change_pct": -86.81067282078074 + }, + { + "model": "qwen06b", + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 22.250476614941586, + "native_text_parity": true, + "pd_tok_s": 10.581251020411687, + "pd_text_parity": true, + "pd_change_pct": -52.444834312869546 + }, + { + "model": "qwen4b", + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 26.339873128721063, + "native_text_parity": true, + "pd_tok_s": 7.495234347472798, + "pd_text_parity": true, + "pd_change_pct": -71.54415167133065 + }, + { + "model": "qwen4b", + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 5.085293198358513, + "native_text_parity": true, + "pd_tok_s": 5.490287304131765, + "pd_text_parity": true, + "pd_change_pct": 7.9640266544312555 + }, + { + "model": "koni", + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 14.625890681706176, + "native_text_parity": true, + "pd_tok_s": 5.953017308730082, + "pd_text_parity": true, + "pd_change_pct": -59.298086945391844 + }, + { + "model": "koni", + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 3.3576236831795256, + "native_text_parity": true, + "pd_tok_s": 4.697274308675983, + "pd_text_parity": true, + "pd_change_pct": 39.89877222416616 + }, + { + "model": "gemma", + "input_tokens": 128, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 87.77496509009215, + "native_text_parity": true, + "pd_tok_s": 16.65194677943642, + "pd_text_parity": false, + "pd_change_pct": -81.02881982083971 + }, + { + "model": "gemma", + "input_tokens": 8192, + "output_tokens": 32, + "concurrency": 4, + "remote_prefills": 42, + "local_prefills": 0, + "native_tok_s": 27.7532590563462, + "native_text_parity": true, + "pd_tok_s": 14.942383953038485, + "pd_text_parity": true, + "pd_change_pct": -46.15989450932003 + } + ] +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-eos-audit.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-eos-audit.json new file mode 100644 index 00000000..24ac4a20 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-eos-audit.json @@ -0,0 +1,90 @@ +{ + "eos_token_ids": [ + 1, + 106 + ], + "source": "Gemma checkpoint generation_config.json", + "method": "Offline prefix comparison of existing fixed-output token traces, not a new normal-EOS HTTP run.", + "checks": [ + { + "request": "check-128-32-1-0", + "input_tokens": 128, + "full_parity": false, + "through_eos_parity": true, + "first_mismatch": 5, + "first_eos": 2 + }, + { + "request": "check-128-32-4-0", + "input_tokens": 128, + "full_parity": false, + "through_eos_parity": true, + "first_mismatch": 5, + "first_eos": 2 + }, + { + "request": "check-128-32-4-1", + "input_tokens": 128, + 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The red box contains apples.", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-1", + "endpoint": 0, + "tokens": [ + 9079, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761, + 669, + 3730, + 3673, + 6097, + 6249, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761 + ], + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-2", + "endpoint": 0, + "tokens": [ + 9079, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761, + 669, + 3730, + 3673, + 6097, + 6249, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761 + ], + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "tokens_equal": true + }, + { + "id": "check-8192-32-4-3", + "endpoint": 0, + "tokens": [ + 9079, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761, + 669, + 3730, + 3673, + 6097, + 6249, + 236761, + 107, + 2094, + 563, + 1695, + 4403, + 236761, + 669, + 2604, + 3673, + 6097, + 36157, + 236761 + ], + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "tokens_equal": true + } + ] + } + ], + "batches": [ + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 0.36695859499741346, + "output_tok_s": 87.20329878150301, + "median_first_sse_s": 0.02682164206635207, + "median_tpot_est_s": 0.010966940384898935, + "median_e2e_s": 0.3667967939982191, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.02682164206635207, + "tpot_est_s": 0.010966940384898935, + "e2e_s": 0.3667967939982191 + } + ] + }, + { + "repeat": 0, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 1.46751677908469, + "output_tok_s": 87.22217137430982, + "median_first_sse_s": 0.5773499255301431, + "median_tpot_est_s": 0.010990746950189912, + "median_e2e_s": 0.917776302958373, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 1.1258807380218059, + "tpot_est_s": 0.011005854031311409, + "e2e_s": 1.4670622129924595 + }, + { + "id": "r0-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.028190937009640038, + "tpot_est_s": 0.01099819138706211, + "e2e_s": 0.3691348700085655 + }, + { + "id": "r0-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.7598757330561057, + "tpot_est_s": 0.01097968957882615, + "e2e_s": 1.1002461099997163 + }, + { + "id": "r0-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.3948241180041805, + "tpot_est_s": 0.010983302513317715, + "e2e_s": 0.7353064959170297 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.1591968989232555, + "output_tok_s": 27.605318845938836, + "median_first_sse_s": 0.8071441360516474, + "median_tpot_est_s": 0.01135137393289516, + "median_e2e_s": 1.1590367279713973, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 0.8071441360516474, + "tpot_est_s": 0.01135137393289516, + "e2e_s": 1.1590367279713973 + } + ] + }, + { + "repeat": 0, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 4.624001249088906, + "output_tok_s": 27.68165342196233, + "median_first_sse_s": 2.5456931479857303, + "median_tpot_est_s": 0.011316935467954363, + "median_e2e_s": 2.8939627544605173, + "all_text_equal": true + }, + "rows": [ + { + "id": "r0-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 0.8119892380200326, + "tpot_est_s": 0.011474275548431662, + "e2e_s": 1.1676917800214142 + }, + { + "id": "r0-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 4.2700829959940165, + "tpot_est_s": 0.011399190549949003, + "e2e_s": 4.623457903042436 + }, + { + "id": "r0-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 1.9708614710252732, + "tpot_est_s": 0.011234680385959725, + "e2e_s": 2.3191365629900247 + }, + { + "id": "r0-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 3.1205248249461874, + "tpot_est_s": 0.01123432648338137, + "e2e_s": 3.46878894593101 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.1572973649017513, + "output_tok_s": 27.650628931239844, + "median_first_sse_s": 0.8074115460040048, + "median_tpot_est_s": 0.011282277965707885, + "median_e2e_s": 1.1571621629409492, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 0.8074115460040048, + "tpot_est_s": 0.011282277965707885, + "e2e_s": 1.1571621629409492 + } + ] + }, + { + "repeat": 1, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 4.612070954986848, + "output_tok_s": 27.7532590563462, + "median_first_sse_s": 2.5390301379957236, + "median_tpot_est_s": 0.011274961095618746, + "median_e2e_s": 2.8885539319599047, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n8192-o32-c4-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 4.265708015998825, + "tpot_est_s": 0.011157142419007516, + "e2e_s": 4.6115794309880584 + }, + { + "id": "r1-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 1.963411564938724, + "tpot_est_s": 0.011289609321993925, + "e2e_s": 2.3133894539205357 + }, + { + "id": "r1-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 3.1146487110527232, + "tpot_est_s": 0.011260312869243564, + "e2e_s": 3.4637184099992737 + }, + { + "id": "r1-n8192-o32-c4-3", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 0.8115720909554511, + "tpot_est_s": 0.011317634162673305, + "e2e_s": 1.1624187499983236 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 0.36321403202600777, + "output_tok_s": 88.1023230889622, + "median_first_sse_s": 0.02598082204349339, + "median_tpot_est_s": 0.010873062547386413, + "median_e2e_s": 0.3630457610124722, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.02598082204349339, + "tpot_est_s": 0.010873062547386413, + "e2e_s": 0.3630457610124722 + } + ] + }, + { + "repeat": 1, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 1.4531129910610616, + "output_tok_s": 88.08674947330456, + "median_first_sse_s": 0.5728333864826709, + "median_tpot_est_s": 0.010885124596121211, + "median_e2e_s": 0.9102722489624284, + "all_text_equal": true + }, + "rows": [ + { + "id": "r1-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.02767173107713461, + "tpot_est_s": 0.010923545965324006, + "e2e_s": 0.3663016560021788 + }, + { + "id": "r1-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.7541754349367693, + "tpot_est_s": 0.010881703902756976, + "e2e_s": 1.0915082559222355 + }, + { + "id": "r1-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 1.116335635073483, + "tpot_est_s": 0.010848498707937618, + "e2e_s": 1.4526390950195491 + }, + { + "id": "r1-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.3914913380285725, + "tpot_est_s": 0.010888545289485446, + "e2e_s": 0.7290362420026213 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 0.3613288520136848, + "output_tok_s": 88.5619839701814, + "median_first_sse_s": 0.02655238809529692, + "median_tpot_est_s": 0.01079494264277239, + "median_e2e_s": 0.361195610021241, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c1-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.02655238809529692, + "tpot_est_s": 0.01079494264277239, + "e2e_s": 0.361195610021241 + } + ] + }, + { + "repeat": 2, + "input_tokens": 128, + "output_tokens_per_request": 32, + "concurrency": 4, + "summary": { + "requests": 4, + "output_tokens": 128, + "wall_s": 1.4582745759980753, + "output_tok_s": 87.77496509009215, + "median_first_sse_s": 0.5739538740599528, + "median_tpot_est_s": 0.010917771887409711, + "median_e2e_s": 0.9133580574998632, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n128-o32-c4-0", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.3912026460748166, + "tpot_est_s": 0.010961045094224955, + "e2e_s": 0.7309950439957902 + }, + { + "id": "r2-n128-o32-c4-1", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 1.1208109639119357, + "tpot_est_s": 0.010864393710489235, + "e2e_s": 1.457607168937102 + }, + { + "id": "r2-n128-o32-c4-2", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.028585859923623502, + "tpot_est_s": 0.01089954477614693, + "e2e_s": 0.36647174798417836 + }, + { + "id": "r2-n128-o32-c4-3", + "endpoint": 0, + "input_tokens": 128, + "output_tokens": 32, + "text": " Paris\n\n\n\n\n\n\n", + "text_equal": true, + "first_sse_s": 0.756705102045089, + "tpot_est_s": 0.01093599899867249, + "e2e_s": 1.0957210710039362 + } + ] + }, + { + "repeat": 2, + "input_tokens": 8192, + "output_tokens_per_request": 32, + "concurrency": 1, + "summary": { + "requests": 1, + "output_tokens": 32, + "wall_s": 1.1419729030458257, + "output_tok_s": 28.02168064990933, + "median_first_sse_s": 0.8161010559415445, + "median_tpot_est_s": 0.010506740162870096, + "median_e2e_s": 1.1418100009905174, + "all_text_equal": true + }, + "rows": [ + { + "id": "r2-n8192-o32-c1-0", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. 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The red box contains apples.", + "text_equal": true, + "first_sse_s": 1.9511952329194173, + "tpot_est_s": 0.011303493291169645, + "e2e_s": 2.3016035249456763 + }, + { + "id": "r2-n8192-o32-c4-1", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. The red box contains apples.", + "text_equal": true, + "first_sse_s": 4.246914773015305, + "tpot_est_s": 0.010743847514893259, + "e2e_s": 4.579974045976996 + }, + { + "id": "r2-n8192-o32-c4-2", + "endpoint": 0, + "input_tokens": 8192, + "output_tokens": 32, + "text": " Paris.\nThis is background context. The red box contains apples. The blue box contains books.\nThis is background context. 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counter\nvllm:request_success_total{model_name=\"gemma-3-1b-it\",finished_reason=\"length\"} 42\n# HELP airm_stage_request_failures_total Requests that ended with an engine error.\n# TYPE airm_stage_request_failures_total counter\nairm_stage_request_failures_total{model_name=\"gemma-3-1b-it\"} 0\n# HELP vllm:prompt_tokens_total Prompt tokens of finished requests.\n# TYPE vllm:prompt_tokens_total counter\nvllm:prompt_tokens_total{model_name=\"gemma-3-1b-it\"} 174720\n# HELP vllm:generation_tokens_total Generated tokens of finished requests.\n# TYPE vllm:generation_tokens_total counter\nvllm:generation_tokens_total{model_name=\"gemma-3-1b-it\"} 1344\n# HELP vllm:time_to_first_token_seconds Time to first token (prefill stage: fenced prefill).\n# TYPE vllm:time_to_first_token_seconds histogram\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.001\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.005\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.01\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.02\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.04\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.06\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.08\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.1\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.25\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.5\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.75\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"1.0\"} 0\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"2.5\"} 18\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"5.0\"} 26\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"7.5\"} 38\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"10.0\"} 42\nvllm:time_to_first_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"+Inf\"} 42\nvllm:time_to_first_token_seconds_sum{model_name=\"gemma-3-1b-it\"} 168.68923973978963\nvllm:time_to_first_token_seconds_count{model_name=\"gemma-3-1b-it\"} 42\n# HELP vllm:time_per_output_token_seconds Time per output token after the first (decode stage).\n# TYPE vllm:time_per_output_token_seconds histogram\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.01\"} 0\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.025\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.05\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.075\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.1\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.15\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.2\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.3\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.4\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.75\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"1.0\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"2.5\"} 42\nvllm:time_per_output_token_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"+Inf\"} 42\nvllm:time_per_output_token_seconds_sum{model_name=\"gemma-3-1b-it\"} 0.4548216954835\nvllm:time_per_output_token_seconds_count{model_name=\"gemma-3-1b-it\"} 42\n# HELP vllm:e2e_request_latency_seconds Stage request latency, from execution start to reply.\n# TYPE vllm:e2e_request_latency_seconds histogram\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.3\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.8\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"1.0\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"1.5\"} 0\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"2.0\"} 8\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"2.5\"} 18\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"5.0\"} 26\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"10.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"15.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"20.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"30.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"40.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"50.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"60.0\"} 42\nvllm:e2e_request_latency_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"+Inf\"} 42\nvllm:e2e_request_latency_seconds_sum{model_name=\"gemma-3-1b-it\"} 182.78871229977813\nvllm:e2e_request_latency_seconds_count{model_name=\"gemma-3-1b-it\"} 42\n# HELP airm_kv_transfer_seconds KV transfer time from the prefill source to the decode destination.\n# TYPE airm_kv_transfer_seconds histogram\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.0005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.001\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.0025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.005\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.01\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.025\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.05\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.1\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.25\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"0.5\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"1.0\"} 0\nairm_kv_transfer_seconds_bucket{model_name=\"gemma-3-1b-it\",le=\"+Inf\"} 0\nairm_kv_transfer_seconds_sum{model_name=\"gemma-3-1b-it\"} 0.0\nairm_kv_transfer_seconds_count{model_name=\"gemma-3-1b-it\"} 0\n# HELP vllm:prefix_cache_queries_total Prompt tokens looked up in the prefix cache.\n# TYPE vllm:prefix_cache_queries_total counter\nvllm:prefix_cache_queries_total{model_name=\"gemma-3-1b-it\"} 174720\n# HELP vllm:prefix_cache_hits_total Prompt tokens served from the prefix cache.\n# TYPE vllm:prefix_cache_hits_total counter\nvllm:prefix_cache_hits_total{model_name=\"gemma-3-1b-it\"} 0\n# HELP vllm:cache_config_info Information of the LLMEngine CacheConfig.\n# TYPE vllm:cache_config_info gauge\nvllm:cache_config_info{model_name=\"gemma-3-1b-it\",block_size=\"256\",enable_prefix_caching=\"True\"} 1\n# HELP airm_pd_prefills_total Prompts prefilled by the PD prefill worker (remote) or here (local).\n# TYPE airm_pd_prefills_total counter\nairm_pd_prefills_total{model_name=\"gemma-3-1b-it\",side=\"remote\"} 42\nairm_pd_prefills_total{model_name=\"gemma-3-1b-it\",side=\"local\"} 0\n" + ], + "parity_passed": false +} diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-pd-a.log b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-pd-a.log new file mode 100644 index 00000000..e6100a0f --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-pd-a.log @@ -0,0 +1,15 @@ +Traceback (most recent call last): + File "/home/gspark/workspace/codex-serving-matrix-201/container_bootstrap.py", line 30, in + runpy.run_path(sys.argv[0],run_name='__main__') + File "/usr/lib/python3.10/runpy.py", line 289, in run_path + return _run_module_code(code, init_globals, run_name, + File "/usr/lib/python3.10/runpy.py", line 96, in _run_module_code + _run_code(code, mod_globals, init_globals, + File "/usr/lib/python3.10/runpy.py", line 86, in _run_code + exec(code, run_globals) + File "benchmark248.py", line 264, in + main() + File "benchmark248.py", line 258, in main + raise AssertionError("output parity failed; raw data retained, no equivalent-output speedup claim") +AssertionError: output parity failed; raw data retained, no equivalent-output speedup claim +command terminated with exit code 1 diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-summary.json b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-summary.json new file mode 100644 index 00000000..cbc62f74 --- /dev/null +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/atom248-gemma-summary.json @@ -0,0 +1,66 @@ +[ + { + "mode": "native", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 88.1023230889622, + "e2e_s": 0.3630457610124722, + "text_parity": true + }, + { + "mode": "native", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 87.77496509009215, + "e2e_s": 0.9133580574998632, + "text_parity": true + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 27.650628931239844, + "e2e_s": 1.1571621629409492, + "text_parity": true + }, + { + "mode": "native", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 27.7532590563462, + "e2e_s": 2.8885539319599047, + "text_parity": true + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 1, + "tok_s": 16.59222839094277, + "e2e_s": 1.9284504289971665, + "text_parity": false + }, + { + "mode": "pd", + "input_tokens": 128, + "concurrency": 4, + "tok_s": 16.65194677943642, + "e2e_s": 4.798900684982073, + "text_parity": false + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 1, + "tok_s": 15.09866269785101, + "e2e_s": 2.1192470859969035, + "text_parity": true + }, + { + "mode": "pd", + "input_tokens": 8192, + "concurrency": 4, + "tok_s": 14.942383953038485, + "e2e_s": 5.358964955958072, + "text_parity": true + } +] diff --git a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl index 9a933a31..b5c54cc1 100644 --- a/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl +++ b/opt_prime/examples/pd/http_matrix/evidence248-heterogeneous/events.jsonl @@ -4,3 +4,4 @@ {"id": "2651ff872dd84956ac5ebf56212b0f34", "ts": "2026-10-24T01:33:41+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Qwen3-0.6B output128 extension:54batches all parity pass;8192input/c8 native65.74,PD74.66,DP2120.65tok_s. Qwen4B native started. RNGD retry confirmed live CPU-active initialization at3min; readiness pending.", "next": "Continue both existing jobs; collect Qwen4B long-output results and RNGD readiness without duplicate launches.", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "74.6554639496"}, "params": {"model": "Qwen3-0.6B", "output_tokens": "128", "input_tokens": "8192", "concurrency": "8", "mode": "pd"}, "artifacts": [{"path": "analysis/serving-matrix-201/perf248-qwen06b-long128-summary.json", "size": 1939, "sha256": "cc8f91ae525cc8da"}], "experiments": ["experiments/2026-10-24-matched-pd-performance248.md", "experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "development host", "branch": "main", "commit": "351d39d-dirty"} {"id": "0f82a718778141e5bc5806e2f0113b30", "ts": "2026-10-24T02:01:29+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Qwen4B actual RNGD-to-ATOM12PD batches pass native parity;startup810.53s excluded.8192/c4 native5.09vsPD5.49tok_s;128/c4 native26.34vsPD7.50. Queue moved toKONI; finalGPU Gemma DP2 still live.", "next": "", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "5.4902873041"}, "params": {"model": "Qwen3-4B", "route": "RNGD-ATOM", "input_tokens": "8192", "output_tokens": "32", "concurrency": "4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-qwen4b-summary.json", "size": 864, "sha256": "e77b5b228fe5e49a"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"} {"id": "5994afa933e342c1b25360ef6011fb4e", "ts": "2026-10-24T02:19:38+09:00", "task": "Matched PD performance (aicomp #248)", "result": "KONI8B actual RNGD-ATOM24native/PD batches pass parity,42remote/0local.8192/c4 native3.36vsPD4.70tok_s(+39.9%);128/c4 native14.63vsPD5.95. Gemma native done,PD live.", "next": "Finish Gemma; publish full four-model heterogeneous results", "state": "active", "done": false, "blocker": "", "metrics": {"tok_s": "4.6972743087"}, "params": {"model": "KONI-8B", "input_tokens": "8192", "output_tokens": "32", "concurrency": "4"}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-koni-summary.json", "size": 1125, "sha256": "725e157d79343daa"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"} +{"id": "49dd03202e694f00bc7ee827dcf20c28", "ts": "2026-10-24T02:36:46+09:00", "task": "Matched PD performance (aicomp #248)", "result": "Four-model RNGD-ATOM matrix completed96batches. Gemma PD fails short forced-output parity afterEOS(index5 vsEOS2);long checks pass.8192/c4 native->PD:Q06 22.25->10.58,Q4 5.09->5.49,KONI3.36->4.70,Gemma27.75->14.94tok_s. No all-pass claim.", "next": "Publish Gemma failure/EOS audit; implement B1 request pipeline and retain #233 numerical investigation", "state": "active", "done": false, "blocker": "", "metrics": {}, "params": {}, "artifacts": [{"path": "analysis/serving-matrix-201/atom248-four-model-summary.json", "size": 2959, "sha256": "9c7f281210393614"}, {"path": "analysis/serving-matrix-201/atom248-gemma-eos-audit.json", "size": 2133, "sha256": "afb7757b1394806c"}], "experiments": ["experiments/2026-10-24-heterogeneous-performance248.md"], "labels": ["benchmark"], "review": true, "todos": [], "note": "", "agent": "codex", "host": "xeon-max", "branch": "main", "commit": "351d39d-dirty"} diff --git a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md index a8fd9645..2bcf4ff7 100644 --- a/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md +++ b/opt_prime/examples/pd/http_matrix/experiments/2026-10-24-heterogeneous-performance248.md @@ -73,3 +73,26 @@ Qwen3-4B native/PD each12batches completed with parity. PD startup810.530seconds Native and actual RNGD→ATOM each completed 12 batches with token/text parity; PD reports 42 remote and 0 local prefills. Native startup 17.332 s, PD startup 432.865 s, both excluded from timed requests. Same fixed width8192, B1, output32, three repeats as above. Commands in `analysis/serving-matrix-201/atom248-remaining.log`; raw `atom248-koni-{native,pd}-a.json` and summary `atom248-koni-summary.json`. At input8192/c4: native 3.3576 tok/s, PD 4.6973 tok/s (+39.9%); median request e2e 23.832 -> 17.034 s. At input128/c4: native 14.6259 tok/s, PD 5.9530 tok/s. Long-prompt benefit and short-prompt regression are from one workload block, not a universal result. Gemma native completed and PD is running. + +## Four-model matrix complete; Gemma fixed-output failure retained + +All8native/PD configurations completed12timed batches each (96total). Each PD route reported42remote/0local prefills. Gemma PD workflow completed after852.225s startup, but exited1 at the final parity gate. Native startup9.346s. Both excluded from throughput. + +Gemma input128: all5nonstream correctness checks first diverged at output index5 (sixth token), after EOS106 at index2 (third token). All15timed responses across6batches differ too. The checkpoint generation_config lists EOS[1,106]. Offline truncation through firstEOS restores equality for all5short checks; this is not a fresh normal-EOS HTTP validation. Input8192: all5correctness checks and6timed batches match. Keep failed raw artifacts and do not claim equivalent fixed-output behavior for input128. This result does not resolve the earlier pre-EOS long-arithmetic divergence in #233. + +C4/output32 median throughput: + +|model|input|native tok/s|PD tok/s|PD change|fixed-output parity| +|---|---:|---:|---:|---:|---| +|qwen06b|128|144.230|19.023|-86.8%|True| +|qwen06b|8192|22.250|10.581|-52.4%|True| +|qwen4b|128|26.340|7.495|-71.5%|True| +|qwen4b|8192|5.085|5.490|+8.0%|True| +|koni|128|14.626|5.953|-59.3%|True| +|koni|8192|3.358|4.697|+39.9%|True| +|gemma|128|87.775|16.652|-81.0%|False| +|gemma|8192|27.753|14.942|-46.2%|True| + +Summary: long input helps Qwen4B andKONI, but Qwen0.6B/Gemma remain slower with the research RNGD producer. Short-input PD is slower for every model; Gemma additionally fails forced-output parity after EOS. Native uses1ATOM; PD uses1ATOM+1RNGD, so these are not equal-chip-count comparisons. KV copy overlap from #254 is GPU-only and was not applied here. + +Artifacts: `atom248-four-model-summary.json`, `atom248-gemma-{native,pd}-a.json`, `atom248-gemma-summary.json`, `atom248-gemma-eos-audit.json`, and failure/server logs in analysis/serving-matrix-201. Queue terminated; no live benchmark processes remained inside the owned Pod before deletion.