diff --git a/extras/benchmark/pyproject.toml b/extras/benchmark/pyproject.toml index 1d2833e57f..cf427d2e09 100644 --- a/extras/benchmark/pyproject.toml +++ b/extras/benchmark/pyproject.toml @@ -3,7 +3,7 @@ # SPDX-License-Identifier: MPL-2.0 [project] -name = "" +name = "traccc-bench-tools" version = "0.0.1" requires-python = "~=3.9" dependencies = [ @@ -15,9 +15,6 @@ dependencies = [ [dependency-groups] dev = ["black>=25.1.0,<26"] -[tool.uv] -package = false - [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/extras/cut_optimiser/.gitignore b/extras/cut_optimiser/.gitignore new file mode 100644 index 0000000000..f27bcbbca3 --- /dev/null +++ b/extras/cut_optimiser/.gitignore @@ -0,0 +1,5 @@ +*.csv +*.csv.bak +*.json +.venv/ +!example.json diff --git a/extras/cut_optimiser/example.json b/extras/cut_optimiser/example.json new file mode 100644 index 0000000000..3de4e1f5d8 --- /dev/null +++ b/extras/cut_optimiser/example.json @@ -0,0 +1,18 @@ +{ + "input": { + "event_dir": "/data/Acts/odd-simulations-20240506/geant4_ttbar_mu200", + "digitization_file": "geometries/odd/odd-digi-geometric-config.json", + "detector_file": "geometries/odd/odd-detray_geometry_detray.json", + "grid_file": "geometries/odd/odd-detray_surface_grids_detray.json", + "material_file": "geometries/odd/odd-detray_material_detray.json" + }, + "config": { + "truth-finding-min-track-candidates": 7, + "track-candidates-range": "7:100" + }, + "parameters": { + "max-num-branches-per-surface": [1, 2, 3], + "max-num-skipping-per-cand": [2, 3], + "chi2-max": [2.5, 5.0, 7.5, 10.0, 15.0, 20.0] + } +} diff --git a/extras/cut_optimiser/find_pareto_set.py b/extras/cut_optimiser/find_pareto_set.py new file mode 100644 index 0000000000..ae8fb5d6fe --- /dev/null +++ b/extras/cut_optimiser/find_pareto_set.py @@ -0,0 +1,97 @@ +import argparse +import csv +import logging +import pathlib + + +log = logging.getLogger("find_pareto_set") + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "db", + type=pathlib.Path, + help="the CSV database file", + ) + + parser.add_argument( + "-v", + "--verbose", + help="enable verbose output", + action="store_true", + ) + + args = parser.parse_args() + + logging.basicConfig( + level=logging.DEBUG if (args.verbose or False) else logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + results = [] + + total_results = 0 + + with open(args.db, "r") as f: + reader = csv.DictReader(f) + for i in reader: + total_results += 1 + if i["success"] != "0": + results.append({k: float(v) for k, v in i.items()}) + + log.info( + "Database contained %d results of which %d are valid", + total_results, + len(results), + ) + + pareto_set = [] + + for i, m in enumerate(results): + for j, n in enumerate(results): + if i == j: + continue + + if ( + n["rec_throughput"] <= m["rec_throughput"] + and n["efficiency"] >= m["efficiency"] + and n["fake_rate"] <= m["fake_rate"] + and n["duplicate_rate"] <= m["duplicate_rate"] + ): + log.debug( + "Removing %s from the Pareto set because %s is superior", + str(n), + str(m), + ) + break + else: + pareto_set.append(m) + + log.info("Pareto set contains %d elements:", len(pareto_set)) + + for i in sorted(pareto_set, key=lambda x: x["rec_throughput"], reverse=True): + log.info( + " Eff. %.2f, fake rate %.2f, duplicate rate %.2f with reciprocal througput %.1fms is achieved by setup {%s}", + 100.0 * i["efficiency"], + i["fake_rate"], + i["duplicate_rate"], + i["rec_throughput"] * 1000.0, + ", ".join( + "%s: %s" % (k, str(v)) + for k, v in i.items() + if k + not in [ + "efficiency", + "fake_rate", + "duplicate_rate", + "rec_throughput", + "success", + ] + ), + ) + + +if __name__ == "__main__": + main() diff --git a/extras/cut_optimiser/main.py b/extras/cut_optimiser/main.py new file mode 100644 index 0000000000..42b2b2fcba --- /dev/null +++ b/extras/cut_optimiser/main.py @@ -0,0 +1,383 @@ +import argparse +import json +import csv +import pathlib +import logging +import functools +import operator +import os +import shutil +import itertools +import random +import tempfile +import time +import subprocess +import re + + +import traccc_bench_tools.parse_profile +import traccc_bench_tools.types + + +log = logging.getLogger("traccc_cut_optimiser") + + +def main(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "exec", + type=pathlib.Path, + help="the traccc ", + ) + + parser.add_argument( + "db", + type=pathlib.Path, + help="the CSV database file", + ) + + parser.add_argument( + "parameters", + type=pathlib.Path, + help="the JSON parameter space file", + ) + + parser.add_argument( + "-v", + "--verbose", + help="enable verbose output", + action="store_true", + ) + + parser.add_argument( + "--num-sm", + help="number of SMs in the modelled GPU", + type=int, + required=True, + dest="num_sm", + ) + + parser.add_argument( + "--num-threads-per-sm", + help="number of thread slots per SM in the modelled GPU", + type=int, + required=True, + dest="num_threads_per_sm", + ) + + parser.add_argument( + "--ncu-wrapper", + help="wrapper to use around the ncu command", + type=str, + dest="ncu_wrapper", + ) + + parser.add_argument( + "--timeout", + help="timeout in seconds for benchmarks", + default=120, + type=int, + ) + + parser.add_argument( + "--random", + help="enable random search rather than grid search", + action="store_true", + ) + + args = parser.parse_args() + + logging.basicConfig( + level=logging.DEBUG if (args.verbose or False) else logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) + + log.info( + "Using GPU with %d SMs and %d thread slots per SM (%d thread slots total)", + getattr(args, "num_sm"), + getattr(args, "num_threads_per_sm"), + getattr(args, "num_sm") * getattr(args, "num_threads_per_sm"), + ) + + gpu_spec = traccc_bench_tools.types.GpuSpec( + getattr(args, "num_sm"), getattr(args, "num_threads_per_sm") + ) + + with open(args.parameters, "r") as f: + params = json.load(f) + + parameter_space = params["parameters"] + parameter_names = sorted(parameter_space) + + log.info( + "Running optimisation for %d parameters: %s", + len(params["parameters"]), + ", ".join(parameter_names), + ) + + csv_field_names = parameter_names + [ + "success", + "rec_throughput", + "efficiency", + "fake_rate", + "duplicate_rate", + ] + + if args.db.is_file(): + log.info('Database file "%s" already exists; creating a backup', args.db) + shutil.copy(str(args.db), str(args.db) + ".bak") + results = {} + with open(args.db, "r") as f: + reader = csv.DictReader(f) + for i in reader: + key = (i[k] for k in parameter_names) + if set(i.keys()) != set(csv_field_names): + raise ValueError( + "Input database has unexpected columns or is missing columns" + ) + if i["success"] != 0: + results[key] = { + "rec_throughput": i["rec_throughput"], + "efficiency": i["efficiency"], + "fake_rate": i["fake_rate"], + "duplicate_rate": i["duplicate_rate"], + } + else: + results[key] = None + log.info("Database contained %d pre-existing results", len(results)) + else: + log.info('Database file "%s" does not exist; starting from scratch', args.db) + results = {} + + log.info("Starting optimisation with %d known results", len(results)) + + log.info( + "Total parameter space has size %d", + functools.reduce(operator.mul, [len(x) for x in parameter_space.values()], 1), + ) + + if "TRACCC_TEST_DATA_DIR" not in os.environ: + e = 'Environment variable "TRACCC_TEST_DATA_DIR" is not set; aborting!' + log.fatal(e) + raise RuntimeError(e) + + for exec in ["ncu"]: + if shutil.which(exec) is None: + e = 'Executable "%s" is not available; aborting' % exec + log.fatal(e) + raise RuntimeError(e) + + space_product = itertools.product( + *[parameter_space[name] for name in parameter_names] + ) + random_retry_count = 0 + it_count = 0 + + while True: + if args.random: + param_dict = {n: random.choice(vs) for n, vs in parameter_space.items()} + param_list = tuple(param_dict.values()) + + if param_list in results: + random_retry_count += 1 + if random_retry_count < 10: + log.info( + "Configuration is already known; continuing with random abort counter %d", + random_retry_count, + ) + continue + else: + log.info( + "Configuration is already known and max retry counter is reached; aborting" + ) + break + else: + random_retry_count = 0 + else: + try: + param_vector = next(space_product) + assert len(param_vector) == len(parameter_names) + param_dict = {parameter_names[i]: p for i, p in enumerate(param_vector)} + param_list = tuple(param_dict.values()) + except StopIteration: + log.info("Design space has been exhausted; exiting") + break + if param_list in results: + log.info( + "Configuration %s is already known; continuing", str(param_dict) + ) + continue + + try: + log.info("Running benchmark for parameters %s", str(param_dict)) + + with tempfile.TemporaryDirectory() as tmpdirname: + tmppath = pathlib.Path(tmpdirname) + + log.info('Created temporary directory "%s"', str(tmppath)) + + log.info("Running benchmark step") + + start_time = time.time() + + profile_args = [ + "ncu", + "--import-source", + "no", + "--section", + "LaunchStats", + "--section", + "Occupancy", + "--metrics", + "gpu__time_duration.sum", + "-f", + "-o", + "profile", + str(args.exec.resolve()), + "--input-directory=%s" % params["input"]["event_dir"], + "--digitization-file=%s" % params["input"]["digitization_file"], + "--detector-file=%s" % params["input"]["detector_file"], + "--grid-file=%s" % params["input"]["grid_file"], + "--material-file=%s" % params["input"]["material_file"], + "--input-events=1", + "--check-performance", + "--use-acts-geom-source=on", + "--use-detray-detector=on", + ] + + for k, v in params["config"].items(): + profile_args.append("--%s=%s" % (k, str(v))) + + for k, v in param_dict.items(): + profile_args.append("--%s=%s" % (k, str(v))) + + if args.ncu_wrapper is not None: + profile_args = args.ncu_wrapper.split() + profile_args + + try: + result = subprocess.run( + profile_args, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + cwd=tmppath, + check=True, + timeout=args.timeout, + ) + except subprocess.CalledProcessError as e: + log.warning("Process failed to execute; continuing") + results[param_list] = None + continue + except subprocess.TimeoutExpired as e: + log.warning("Process timed out; marking as failure and continuing") + results[param_list] = None + continue + + stdout = result.stdout.decode("utf-8") + + if match := re.search(r"Total efficiency was (\d+(?:\.\d*)?)%", stdout): + result_efficiency = float(match.group(1)) / 100.0 + else: + raise ValueError("Efficiency could not be parsed from stdout!") + + if match := re.search( + r"Total duplicate rate was (\d+(?:\.\d*)?)", stdout + ): + result_duplicate_rate = float(match.group(1)) + else: + raise ValueError("Dupliacate rate could not be parsed from stdout!") + + if match := re.search(r"Total fake rate was (\d+(?:\.\d*)?)", stdout): + result_fake_rate = float(match.group(1)) + else: + raise ValueError("Fake rate could not be parsed from stdout!") + + log.info( + "Physics performance was %.1f%% efficiency, %.1f fake rate, %.1f duplicate rate", + result_efficiency * 100.0, + result_duplicate_rate, + result_fake_rate, + ) + + end_time = time.time() + + log.info( + "Completed benchmark step in %.1f seconds", end_time - start_time + ) + + log.info("Running profile processing step") + + start_time = time.time() + + result_df = traccc_bench_tools.parse_profile.parse_profile_ncu( + tmppath / "profile.ncu-rep", + gpu_spec, + event_marker_kernel="count_grid_capacities", + ) + + total_rec_throughput = result_df["RecThroughputMean"].sum() + + log.info( + "Compute performance was a combined reciprocal throughput of %.1fms", + 1000.0 * total_rec_throughput, + ) + + end_time = time.time() + + log.info( + "Completed profile processing step in %.1f seconds", + end_time - start_time, + ) + + results[param_list] = { + "rec_throughput": total_rec_throughput, + "efficiency": result_efficiency, + "fake_rate": result_fake_rate, + "duplicate_rate": result_duplicate_rate, + } + + except Exception as e: + log.exception(e) + results[param_list] = None + except KeyboardInterrupt as e: + log.info("Received keyboard interrupt; skipping to post-processing") + break + + log.info("Gathered a total of %d results (incl. pre-existing)", len(results)) + + log.info("Writing data to %s", args.db) + with open(args.db, "w") as f: + writer = csv.DictWriter( + f, + fieldnames=csv_field_names, + ) + writer.writeheader() + + for k, v in results.items(): + + param_dict = {parameter_names[i]: p for i, p in enumerate(k)} + if v is None: + writer.writerow( + dict( + **param_dict, + success=0, + rec_throughput=0.0, + efficiency=0.0, + fake_rate=0.0, + duplicate_rate=0.0, + ) + ) + else: + writer.writerow( + dict( + **param_dict, + **v, + success=1, + ) + ) + + log.info("Processing complete; goodbye!") + + +if __name__ == "__main__": + main() diff --git a/extras/cut_optimiser/pyproject.toml b/extras/cut_optimiser/pyproject.toml new file mode 100644 index 0000000000..8deb050903 --- /dev/null +++ b/extras/cut_optimiser/pyproject.toml @@ -0,0 +1,18 @@ +[project] +name = "cut-optimiser" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.13" +dependencies = [ + "pydantic>=2.11.7", + "traccc-bench-tools", +] + +[tool.uv.sources] +traccc-bench-tools = { path = "../benchmark" } + +[dependency-groups] +dev = [ + "black>=25.1.0", +] diff --git a/extras/cut_optimiser/uv.lock b/extras/cut_optimiser/uv.lock new file mode 100644 index 0000000000..76e7bb9b93 --- /dev/null +++ b/extras/cut_optimiser/uv.lock @@ -0,0 +1,513 @@ +version = 1 +revision = 1 +requires-python = ">=3.13" + +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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