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[MetaSchedule] Distributed Measurement
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python/tvm/meta_schedule/testing/distributed_measure_candidates.py
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| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you under the Apache License, Version 2.0 (the | ||
| # "License"); you may not use this file except in compliance | ||
| # with the License. You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an | ||
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| # KIND, either express or implied. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
| # pylint: disable=missing-docstring | ||
|
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| import argparse | ||
| import glob | ||
| import os | ||
| import time | ||
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| from tqdm import tqdm # type: ignore | ||
| from tvm import meta_schedule as ms | ||
| from tvm.target import Target | ||
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| def _parse_args(): | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "--candidate_cache_dir", type=str, help="Please provide the full path to the candidates." | ||
| ) | ||
| parser.add_argument( | ||
| "--result_cache_dir", type=str, help="Please provide the full path to the result database." | ||
| ) | ||
| parser.add_argument( | ||
| "--target", | ||
| type=str, | ||
| default="nvidia/nvidia-v100", | ||
| help="Please specify the target hardware for tuning context.", | ||
| ) | ||
| parser.add_argument( | ||
| "--rpc_host", type=str, help="Please provide the private IPv4 address for the tracker." | ||
| ) | ||
| parser.add_argument( | ||
| "--rpc_port", type=int, default=4445, help="Please provide the port for the tracker." | ||
| ) | ||
| parser.add_argument( | ||
| "--rpc_key", | ||
| type=str, | ||
| default="p3.2xlarge", | ||
| help="Please provide the key for the rpc servers.", | ||
| ) | ||
| parser.add_argument( | ||
| "--builder_timeout_sec", | ||
| type=int, | ||
| default=10, | ||
| help="The time for the builder session to time out.", | ||
| ) | ||
| parser.add_argument( | ||
| "--min_repeat_ms", type=int, default=100, help="The time for preheating the gpu." | ||
| ) | ||
| parser.add_argument( | ||
| "--runner_timeout_sec", | ||
| type=int, | ||
| default=100, | ||
| help="The time for the runner session to time out.", | ||
| ) | ||
| return parser.parse_args() | ||
|
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|
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| # pylint: disable=too-many-locals | ||
| def measure_candidates(database, builder, runner): | ||
| """Send the candidates to builder and runner for distributed measurement, | ||
| and save the results in a new json database. | ||
| Parameters | ||
| ---------- | ||
| database : JSONDatabase | ||
| The database for candidates to be measured. | ||
| builder : Builder | ||
| The builder for building the candidates. | ||
| runner : Runner | ||
| The runner for measuring the candidates. | ||
| Returns | ||
| ------- | ||
| None | ||
| """ | ||
| context = ms.TuneContext(target=Target(args.target)) | ||
| tuning_records = database.get_all_tuning_records() | ||
| candidates = [] | ||
| for record in tuning_records: | ||
| candidate = context.get_measure_candidate(record.workload.mod, record.trace) | ||
| candidates.append(candidate) | ||
| context.set_measure_candidates(candidates) | ||
|
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| build_start_time = time.time() | ||
| context.send_to_builder(builder) | ||
| build_end_time = time.time() | ||
| build_fail_indices = [] | ||
| for i, result in enumerate(context.builder_results): | ||
| if result.error_msg is not None: | ||
| build_fail_indices.append(i) | ||
| print( | ||
| f"Builder time: {build_end_time - build_start_time}\n\ | ||
| Failed number of builds: {len(build_fail_indices)}" | ||
| ) | ||
|
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| context.send_to_runner(runner) | ||
| runner_results = context.join() | ||
| for result in context.builder_results: | ||
| ms.utils.remove_build_dir(result.artifact_path) | ||
| context.clear_measure_state() | ||
| run_end_time = time.time() | ||
| run_fail_indices = [] | ||
| for i, result in enumerate(runner_results): | ||
| if result.error_msg is not None: | ||
| run_fail_indices.append(i) | ||
| print( | ||
| f"Runner time: {run_end_time - build_end_time}\n\ | ||
| Failed number of runs: {len(run_fail_indices)}" | ||
| ) | ||
|
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| model_name, workload_name = database.path_workload.split("/")[-2:] | ||
| record_name = database.path_tuning_record.split("/")[-1] | ||
| new_database = ms.database.JSONDatabase( | ||
| path_workload=os.path.join(args.result_cache_dir, model_name, workload_name), | ||
| path_tuning_record=os.path.join(args.result_cache_dir, model_name, record_name), | ||
| ) | ||
| workload = tuning_records[0].workload | ||
| new_database.commit_workload(workload.mod) | ||
| for record, result in zip(tuning_records, runner_results): | ||
| if result.error_msg is None: | ||
| new_database.commit_tuning_record( | ||
| ms.database.TuningRecord( | ||
| trace=record.trace, | ||
| workload=workload, | ||
| run_secs=[v.value for v in result.run_secs], | ||
| target=Target(args.target), | ||
| ) | ||
| ) | ||
| fail_indices_name = workload_name[:-13] + "failed_indices.txt" | ||
| with open( | ||
| os.path.join(args.result_cache_dir, model_name, fail_indices_name), "w", encoding="utf8" | ||
| ) as file: | ||
| file.write(" ".join(run_fail_indices)) | ||
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| args = _parse_args() # pylint: disable=invalid-name | ||
|
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| def main(): | ||
| builder = ms.builder.LocalBuilder(timeout_sec=args.builder_timeout_sec) | ||
| runner = ms.runner.RPCRunner( | ||
| rpc_config=ms.runner.RPCConfig( | ||
| tracker_host=args.rpc_host, | ||
| tracker_port=args.rpc_port, | ||
| tracker_key=args.rpc_key, | ||
| session_timeout_sec=args.runner_timeout_sec, | ||
| ), | ||
| evaluator_config=ms.runner.EvaluatorConfig( | ||
| number=1, | ||
| repeat=1, | ||
| min_repeat_ms=args.min_repeat_ms, | ||
| enable_cpu_cache_flush=False, | ||
| ), | ||
| max_workers=os.cpu_count(), | ||
| ) | ||
| if not os.path.isdir(args.candidate_cache_dir): | ||
| raise Exception("Please provide a correct candidate cache dir.") | ||
| try: | ||
| os.makedirs(args.result_cache_dir, exist_ok=True) | ||
| except OSError: | ||
| print(f"Directory {args.result_cache_dir} cannot be created successfully.") | ||
| model_dirs = glob.glob(os.path.join(args.candidate_cache_dir, "*")) | ||
| for model_dir in model_dirs: | ||
| model_name = model_dir.split("/")[-1] | ||
| os.makedirs(os.path.join(args.result_cache_dir, model_name), exist_ok=True) | ||
| all_tasks = glob.glob(os.path.join(model_dir, "*.json")) | ||
| workload_paths = [] | ||
| for path in all_tasks: | ||
| if "workload" in path: | ||
| workload_paths.append(path) | ||
| for workload_path in tqdm(workload_paths): | ||
| candidate_path = workload_path[:-13] + "candidates.json" | ||
| database = ms.database.JSONDatabase( | ||
| path_workload=workload_path, | ||
| path_tuning_record=candidate_path, | ||
| ) | ||
| measure_candidates(database, builder, runner) | ||
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| if __name__ == "__main__": | ||
| main() |
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