diff --git a/CHANGELOG.md b/CHANGELOG.md index 13b269d..6378add 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,6 +1,10 @@ # Changelog ## [Unreleased] +### Fixed +- **TabICL compiled new graphs inside requests** (#47): call shapes warmup didn't cover -- tables of ~20k+ rows (differently strided inputs), 1,024+-row regression predicts, and variants that overlapping calls on one worker produce -- each compiled 1-7 graphs inside the request that hit them, 5-25 s on an A100 and ~4x that on prod's A40s, holding the device so other models' calls waited too. On prod, a 5,000 x 500 TabICL fit compiled 3 graphs on every call (25.8 s, `timings_ms` from 0.14.0). Once every backend in a process has warmed up (`serve/factory.finish_warmup`: after all devices of an in-process engine, or a worker process's one), a call no compiled graph fits now runs eagerly instead (`torch.compiler.set_stance("eager_on_recompile")`, process-wide, only where compiled blocks were installed). On an A100: a first 20,000 x 100 fit 12.5 s -> 3.0 s, a first 1,000-row regression predict 1.8 s -> 0.14 s; eager costs ~30% on those families once a compile would have been paid (20,000 x 100 2.3 s compiled vs 2.9 s eager). `TABCTX_COMPILE_AFTER_WARMUP=1` restores compiling them. +### Added +- `prod_bench.py --log-calls` saves every call with its `timings_ms`, for timelines of where stalls go. ## [0.14.0] - 2026-10-21 ### Added diff --git a/benchmarks/baselines/prod-bench-0.14.0.json b/benchmarks/baselines/prod-bench-0.14.0.json new file mode 100644 index 0000000..750ab35 --- /dev/null +++ b/benchmarks/baselines/prod-bench-0.14.0.json @@ -0,0 +1,22598 @@ +{ + "label": "0.14.0", + "tag": "df3e23", + "timestamp": "2026-10-21T03:29:48.124497+00:00", + "base_url": "https://proxy.vectorinstitute.ai/v1", + "stall_s": 5.0, + "latency": { + "tabicl-v2 1000x10": { + "first_fit_s": 0.58, + "repeat_fit_s": 0.514, + "predict_p50_s": 0.131 + }, + "tabicl-v2 10000x10": { + "first_fit_s": 1.178, + 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"predict_device": 549.3, + "n_predict_chunks": 1, + "n_batched_with": 1 + } + }, + { + "end_s": 944.54, + "s": 5.138, + "call": "predict tabpfn-3.5", + "timings_ms": { + "read_table": 18.1, + "coalesce_wait": 6.9, + "worker_call": 4760.0, + "wait_host_memory": 0.0, + "wait_device": 4208.0, + "predict_device": 551.5, + "n_predict_chunks": 1, + "n_batched_with": 1 + } + }, + { + "end_s": 945.0, + "s": 5.559, + "call": "predict tabicl-v2", + "timings_ms": { + "read_table": 15.9, + "coalesce_wait": 5.1, + "worker_call": 5207.8, + "wait_host_memory": 0.2, + "wait_device": 4736.4, + "predict_device": 470.2, + "n_predict_chunks": 1, + "n_batched_with": 1 + } + } + ] +} diff --git a/benchmarks/prod_bench.py b/benchmarks/prod_bench.py index aee760c..fef036a 100644 --- a/benchmarks/prod_bench.py +++ b/benchmarks/prod_bench.py @@ -191,6 +191,11 @@ def summary(self) -> dict | None: } +CALL_LOG: list[dict] | None = None # every call, when --log-calls is given +_CALL_LOG_LOCK = threading.Lock() +_START = time.monotonic() + + class Slowest: """The slowest calls of a phase with their server timing breakdown (timings_ms, from tabctx 0.14), to say where the time went.""" @@ -201,6 +206,16 @@ def __init__(self, keep: int = 10) -> None: self._lock = threading.Lock() def add(self, seconds: float, what: str, body: dict) -> None: + if CALL_LOG is not None: + with _CALL_LOG_LOCK: + CALL_LOG.append( + { + "end_s": round(time.monotonic() - _START, 2), + "s": round(seconds, 3), + "call": what, + "timings_ms": body.get("timings_ms"), + } + ) with self._lock: self._calls.append((seconds, what, body.get("timings_ms"))) self._calls.sort(key=lambda c: -c[0]) @@ -357,7 +372,13 @@ def main() -> int: p.add_argument("--threads", type=int, default=16) p.add_argument("--dashboard", default="http://localhost:8265") p.add_argument("--save") + p.add_argument( + "--log-calls", action="store_true", help="save every call with its timings_ms" + ) args = p.parse_args() + global CALL_LOG + if args.log_calls: + CALL_LOG = [] tag = uuid.uuid4().hex[:6] client = Client(args.base_url, os.environ["VP_API_KEY"]) @@ -378,6 +399,8 @@ def main() -> int: client, tag, args.throughput_s, args.threads, poller ) result["rate_limited"] = client.rate_limited + if CALL_LOG is not None: + result["calls"] = CALL_LOG if args.save: Path(args.save).write_text(json.dumps(result, indent=2) + "\n") print(f"done; rate-limited requests: {client.rate_limited}", flush=True) diff --git a/src/tabctx/backends/tabicl.py b/src/tabctx/backends/tabicl.py index bdaff85..f120616 100644 --- a/src/tabctx/backends/tabicl.py +++ b/src/tabctx/backends/tabicl.py @@ -585,7 +585,8 @@ def warmup(self) -> None: # (rows, features): with compiled blocks, tabicl's fp16 autocast path # (1,024+ rows; the only one compiled), including a 1-feature table # (dynamo specializes size-1 dimensions, so they get their own - # graphs). + # graphs). Families these don't cover run eagerly once every backend + # in the process has warmed up (serve/factory.finish_warmup). shapes = ((32, 4), (1100, 4), (1100, 1)) if self._compiled else ((32, 4),) for rows, features in shapes: X = rng.normal(size=(rows, features)) diff --git a/src/tabctx/backends/tabicl_compile.py b/src/tabctx/backends/tabicl_compile.py index 09221e6..688dce0 100644 --- a/src/tabctx/backends/tabicl_compile.py +++ b/src/tabctx/backends/tabicl_compile.py @@ -27,11 +27,16 @@ path (tables of 1,024+ rows) is compiled: smaller tables run in fp32 in ~0.1 s and would double the graphs. ~50 graphs compile in the backend's warmup at replica start (~130 s per worker on an A100, workers in -parallel; Inductor's cache barely shortens it, tracing dominates). Calls -warmup doesn't cover still compile inside a request, once per worker: -tables of ~30k rows hand the blocks differently strided inputs (1-7 more -graphs, 5-25 s on an A100 for the first such fit, 10,000 x 500 one more), -and other unusual shapes may too. +parallel; Inductor's cache barely shortens it, tracing dominates). + +After warmup nothing compiles inside a request (stop_compiling_new_graphs, +called by serve/factory.finish_warmup once every backend in the process is warm): +a call no warmed graph fits runs eagerly. Families warmup can't foresee +remain -- tables of ~20k+ rows hand the blocks differently strided inputs, +regression predicts of 1,024+ rows take the fp16 path, and calls +overlapping on one worker produce variants -- and compiling them in +requests cost 45-200 s fits on prod's A40s. TABCTX_COMPILE_AFTER_WARMUP=1 +restores compiling them. Failure. Compilation errors fall back to eager (dynamo's suppress_errors), so a missing compiler toolchain costs speed, not availability. @@ -129,6 +134,40 @@ def compile_settings(devices: int) -> dict[str, Any]: } +def compiled_blocks_installed() -> bool: + """Whether install_compiled_blocks ran in this process.""" + try: + from tabicl._model.layers import MultiheadAttentionBlock + except ImportError: + return False + return bool(getattr(MultiheadAttentionBlock.forward, "_tabctx_compiled", False)) + + +def stop_compiling_new_graphs() -> bool: + """After warmup: run calls that no compiled graph fits eagerly instead + of compiling one inside the request (torch.compiler stance + "eager_on_recompile", process-wide). A compile costs 5-25 s per graph + family on an A100 and ~4x that on an A40, and a request can need + several (larger tables, other predict sizes, overlapping calls); on + prod that was 45-200 s fits under light load, holding the device so + other models' calls waited too (tabctx 0.14 timings_ms). Eager for a + family warmup missed costs ~1.3x its compiled time instead (A100, + 20,000-40,000-row and 5,000 x 500 fits). Process-wide: it affects every + torch.compile in the process, so it is set once all of them have warmed + up (serve/factory.finish_warmup). Returns False on a torch without + stances (compiling continues as before).""" + try: + import torch + except ImportError: + return False + set_stance = getattr(getattr(torch, "compiler", None), "set_stance", None) + if set_stance is None: + return False + set_stance("eager_on_recompile") + logger.info("tabicl: warmup done; uncovered call shapes run eagerly from now on") + return True + + def warn_if_not_compiled() -> bool: """After warmup: warn if no graph was compiled (compile errors fall back to eager silently, losing the speedup). Returns whether compilation diff --git a/src/tabctx/serve/factory.py b/src/tabctx/serve/factory.py index c4422d8..818e2f2 100644 --- a/src/tabctx/serve/factory.py +++ b/src/tabctx/serve/factory.py @@ -65,6 +65,12 @@ host-bound, so threads contend for the GIL: measured on 4x A100, four concurrent fits took 2x as long each from threads, single-GPU time from processes. +- ``TABCTX_COMPILE``: ``1`` (default) compiles TabICL's attention blocks + at warmup. ``TABCTX_COMPILE_AFTER_WARMUP``: ``0`` (default) runs call + shapes warmup didn't cover eagerly (process-wide, once every backend has + warmed up: any torch.compile in the process stops compiling new graphs); + ``1`` compiles them inside the requests that hit them (5-25 s per graph + family on an A100, ~4x on an A40; see backends/tabicl_compile.py). """ from __future__ import annotations @@ -117,6 +123,7 @@ DEVICES_ENV_VAR = "TABCTX_DEVICES" WORKER_PROCESSES_ENV_VAR = "TABCTX_WORKER_PROCESSES" COMPILE_ENV_VAR = "TABCTX_COMPILE" +COMPILE_AFTER_WARMUP_ENV_VAR = "TABCTX_COMPILE_AFTER_WARMUP" FAST_PREPROCESSING_ENV_VAR = "TABCTX_FAST_PREPROCESSING" logger = logging.getLogger(__name__) @@ -148,6 +155,10 @@ class ServeSettings: # torch.compile the models' hot blocks where measured to help (TabICL's # attention blocks: backends/tabicl_compile.py). compile_models: bool = True + # Compile graphs for call shapes warmup didn't cover, inside the + # requests that hit them, instead of running those eagerly (see + # tabicl_compile.stop_compiling_new_graphs). + compile_after_warmup: bool = False # Vectorized (GPU) Yeo-Johnson fits in TabICL's preprocessing # (backends/yeo_johnson.py). fast_preprocessing: bool = True @@ -241,6 +252,9 @@ def from_env(cls) -> ServeSettings: offload_dir=offload_dir, worker_processes=_env_flag(WORKER_PROCESSES_ENV_VAR, cls.worker_processes), compile_models=_env_flag(COMPILE_ENV_VAR, cls.compile_models), + compile_after_warmup=_env_flag( + COMPILE_AFTER_WARMUP_ENV_VAR, cls.compile_after_warmup + ), fast_preprocessing=_env_flag( FAST_PREPROCESSING_ENV_VAR, cls.fast_preprocessing ), @@ -726,6 +740,25 @@ def build_shard( ) +def finish_warmup(settings: ServeSettings) -> None: + """Call once every backend in this process has warmed up: from then on, + calls no compiled graph fits run eagerly rather than compiling inside a + request (tabicl_compile.stop_compiling_new_graphs). Process-wide, so it + must wait for the last warmup -- an in-process engine over several + devices warms each device in turn, and graphs are per device.""" + if not settings.compile_models or settings.compile_after_warmup: + return + from tabctx.backends.tabicl_compile import ( + compiled_blocks_installed, + stop_compiling_new_graphs, + ) + + # Only a process that compiled something: on a CPU (or with no tabicl) + # there is nothing to protect, and the stance would outlive this engine. + if compiled_blocks_installed(): + stop_compiling_new_graphs() + + def build_engine(settings: ServeSettings | None = None) -> BuiltEngine: settings = settings or ServeSettings.from_env() devices = resolve_devices(settings) @@ -758,6 +791,8 @@ def build_engine(settings: ServeSettings | None = None) -> BuiltEngine: ) for device in devices ] + if not fake_only: + finish_warmup(settings) first = shards[0] engine: TabctxEngine | MultiDeviceEngine = ( MultiDeviceEngine([s.engine for s in shards], list(devices)) diff --git a/src/tabctx/serve/workers.py b/src/tabctx/serve/workers.py index 9b72805..259fad2 100644 --- a/src/tabctx/serve/workers.py +++ b/src/tabctx/serve/workers.py @@ -210,7 +210,7 @@ def _worker_main( """Entry point of a worker process: build the device's engine, then serve calls until the pipe closes.""" logging.basicConfig(level=logging.INFO) - from tabctx.serve.factory import build_shard + from tabctx.serve.factory import build_shard, finish_warmup if host_guard is not None: host_guard.bind(index) # so the parent can release it if we die @@ -226,6 +226,8 @@ def _worker_main( except BaseException as e: # noqa: BLE001 -- reported to the parent conn.send(_Failure(_portable(e))) return + if set(settings.backends) != {"fake"}: + finish_warmup(settings) # this process's only shard is warm engine = shard.engine seq = itertools.count(1) seq_lock = threading.Lock() diff --git a/tests/unit/test_serve_factory.py b/tests/unit/test_serve_factory.py index e69384f..1bd2b72 100644 --- a/tests/unit/test_serve_factory.py +++ b/tests/unit/test_serve_factory.py @@ -516,3 +516,31 @@ def need(grid, n, d): _requirement_calibration("tabpfn", "kv", True)[1] == (_requirement_calibration("tabpfn", "kv", False)[1]) ) + + +def test_compile_after_warmup_is_off_unless_asked(monkeypatch): + from tabctx.serve.factory import COMPILE_AFTER_WARMUP_ENV_VAR + + monkeypatch.delenv(COMPILE_AFTER_WARMUP_ENV_VAR, raising=False) + assert ServeSettings.from_env().compile_after_warmup is False + monkeypatch.setenv(COMPILE_AFTER_WARMUP_ENV_VAR, "1") + assert ServeSettings.from_env().compile_after_warmup is True + + +@pytest.mark.parametrize( + "installed, compile_after_warmup, flips", + [(True, False, True), (False, False, False), (True, True, False)], +) +def test_finish_warmup_flips_the_stance_only_for_a_compiling_process( + monkeypatch, installed, compile_after_warmup, flips +): + from tabctx.backends import tabicl_compile + from tabctx.serve.factory import finish_warmup + + calls = [] + monkeypatch.setattr(tabicl_compile, "compiled_blocks_installed", lambda: installed) + monkeypatch.setattr( + tabicl_compile, "stop_compiling_new_graphs", lambda: calls.append(1) + ) + finish_warmup(ServeSettings(compile_after_warmup=compile_after_warmup)) + assert bool(calls) is flips diff --git a/tests/unit/test_tabicl_compile.py b/tests/unit/test_tabicl_compile.py index da3f660..c5597af 100644 --- a/tests/unit/test_tabicl_compile.py +++ b/tests/unit/test_tabicl_compile.py @@ -115,3 +115,30 @@ def f(x): thread.join() assert counters["stats"]["unique_graphs"] == graphs + 1 dynamo.reset() + + +def test_after_warmup_uncovered_shapes_run_eagerly_instead_of_compiling(): + from torch._dynamo import eval_frame + from torch._dynamo.utils import counters + + from tabctx.backends.tabicl_compile import stop_compiling_new_graphs + + @torch.compile(backend="eager", dynamic=False) + def f(x): + return x * 2 + 1 + + f(torch.ones(3)) # one graph, as warmup would leave + graphs = counters["stats"]["unique_graphs"] + before = eval_frame._stance.stance + try: + assert stop_compiling_new_graphs() is True + assert torch.equal(f(torch.ones(3)), torch.full((3,), 3.0)) # cached graph + assert torch.equal(f(torch.ones(5)), torch.full((5,), 3.0)) # new shape: eager + results = [] + thread = threading.Thread(target=lambda: results.append(f(torch.ones(7)))) + thread.start() + thread.join() + assert torch.equal(results[0], torch.full((7,), 3.0)) # another thread too + assert counters["stats"]["unique_graphs"] == graphs # nothing compiled + finally: + torch.compiler.set_stance(before)