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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/06/11 11:56:41 UTC
[GitHub] [tvm] Kathryn-cat opened a new pull request, #11683: [MetaSchedule] Distributed Measurement
Kathryn-cat opened a new pull request, #11683:
URL: https://github.com/apache/tvm/pull/11683
In this draft PR, I exhibit some progress on distributed measurement of candidates using builder and async runner. The current progress is a basic example of using builder and runner as well as some auxiliary functions.
Some of the next steps are:
- [ ] Use JsonDatabase with @junrushao1994 's fast json parser for loading and storage.
- [ ] Simplify the method used for creating candidates of type `MeasureCandidate`.
- [ ] Figure out the runner issue.
- [ ] Scale up the distributed measurement process.
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899910272
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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)
Review Comment:
to be less error-prone, can we do like:
```python
if path.endswith("_workload.json"):
```
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899950118
##########
src/meta_schedule/tune_context.cc:
##########
@@ -70,6 +70,19 @@ void TuneContextNode::Initialize() {
}
}
+MeasureCandidate TuneContextNode::_GetMeasureCandidate(const IRModule& mod,
+ const tir::Trace& trace) {
+ tir::Schedule sch = tir::Schedule::Traced(mod, -1, 0, tir::ScheduleErrorRenderLevel::kDetail);
+ trace->ApplyToSchedule(sch, false, nullptr);
+ tir::PrimFunc func;
+ for (const auto& kv : sch->mod()->functions) {
+ func = Downcast<tir::PrimFunc>(kv.second);
+ }
+ Array<ArgInfo> args_info = ArgInfo::FromPrimFunc(func);
+ MeasureCandidate candidate = MeasureCandidate(sch, args_info);
+ return candidate;
+}
Review Comment:
thanks! addressed :-)
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899961398
##########
python/tvm/meta_schedule/tune_context.py:
##########
@@ -287,3 +287,64 @@ def notify_runner_results(
"Please construct TuneContext with search_strategy"
)
return self.search_strategy.notify_runner_results(measure_candidates, results)
+
+ def get_measure_candidate(self, mod, trace):
+ """Generate a measure candidate given an initial IR module and a trace.
+
+ Parameters
+ -----------
+ mod : IRModule
+ The initial IR module.
+ trace : Trace
+ The trace applying to the IR Module.
+
+ Returns
+ -------
+ candidate : MeasureCandidate
+ A generated candidate.
+ """
+ return _ffi_api.TuneContextGetMeasureCandidate(self, mod, trace) # type: ignore # pylint: disable=no-member
+
+ def set_measure_candidates(self, candidates):
Review Comment:
Thanks! addressed ;)
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899910739
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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"
Review Comment:
```suggestion
candidate_path = workload_path.replace("_workload.json", "_candidates.json")
```
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899909565
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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,
Review Comment:
Let's have an argparse argument for this given it should be turned on when measuring with CPU. See this for examples: https://github.com/apache/tvm/blob/main/python/tvm/meta_schedule/testing/tune_relay.py#L96-L100
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899900392
##########
src/meta_schedule/tune_context.cc:
##########
@@ -70,6 +70,19 @@ void TuneContextNode::Initialize() {
}
}
+MeasureCandidate TuneContextNode::_GetMeasureCandidate(const IRModule& mod,
+ const tir::Trace& trace) {
+ tir::Schedule sch = tir::Schedule::Traced(mod, -1, 0, tir::ScheduleErrorRenderLevel::kDetail);
+ trace->ApplyToSchedule(sch, false, nullptr);
+ tir::PrimFunc func;
+ for (const auto& kv : sch->mod()->functions) {
+ func = Downcast<tir::PrimFunc>(kv.second);
+ }
+ Array<ArgInfo> args_info = ArgInfo::FromPrimFunc(func);
+ MeasureCandidate candidate = MeasureCandidate(sch, args_info);
+ return candidate;
+}
Review Comment:
Note that this method isn't using any fields of the TuneContext, and therefore TuneContext might not be the best place for it to be attached to.
Let's think of a possible alternative: We are only using this method when converting a `TuningRecord` to a `MeasureCandidate` in `distributed_measure_candidates.py`, so how about we move this to `TuningRecordNode::AsMeasureCandidate()`?
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900013604
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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)
Review Comment:
Thanks! addressed :)
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900012937
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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,
Review Comment:
Thanks! addressed :)
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900009929
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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,
Review Comment:
Thanks! edited :)
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899907240
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
Review Comment:
There is a cool thing called `ms.Profiler` which you might be interested in! In our usecase, it could be as easy as:
```python
with ms.Profiler() as profiler:
for each batch:
with ms.Profiler.timeit("build"):
...
...
with ms.Profiler.timeit("run"):
...
for seg_name, seg_total_time in profiler.get():
print(...)
```
See the unitttest for examples: https://github.com/apache/tvm/blob/main/tests/python/unittest/test_meta_schedule_profiler.py#L24-L36
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899907240
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
Review Comment:
There is a cool thing called `ms.Profiler` which you might be interested in! In our usecase, it could be as easy as:
```python
with ms.Profiler() as profiler:
for each batch:
with ms.Profiler.timeit("build"):
...
...
with ms.Profiler.timeit("run"):
...
for seg_name, seg_total_time in profiler.get():
print(...)
```
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899901175
##########
python/tvm/meta_schedule/tune_context.py:
##########
@@ -287,3 +287,64 @@ def notify_runner_results(
"Please construct TuneContext with search_strategy"
)
return self.search_strategy.notify_runner_results(measure_candidates, results)
+
+ def get_measure_candidate(self, mod, trace):
+ """Generate a measure candidate given an initial IR module and a trace.
+
+ Parameters
+ -----------
+ mod : IRModule
+ The initial IR module.
+ trace : Trace
+ The trace applying to the IR Module.
+
+ Returns
+ -------
+ candidate : MeasureCandidate
+ A generated candidate.
+ """
+ return _ffi_api.TuneContextGetMeasureCandidate(self, mod, trace) # type: ignore # pylint: disable=no-member
+
+ def set_measure_candidates(self, candidates):
Review Comment:
I'm not super opinionated, but given those are private APIs in C++, shall we add an underscore prefix to them to be consistent with C++ side?
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900014552
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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"
Review Comment:
Thanks! addressed :)
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900009929
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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,
Review Comment:
I think we might want to measure only once though. What do you think?
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[GitHub] [tvm] Kathryn-cat commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
Kathryn-cat commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r900004263
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
Review Comment:
Thanks! addressed :-)
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[GitHub] [tvm] junrushao1994 merged pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 merged PR #11683:
URL: https://github.com/apache/tvm/pull/11683
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[GitHub] [tvm] junrushao1994 commented on a diff in pull request #11683: [MetaSchedule] Distributed Measurement
Posted by GitBox <gi...@apache.org>.
junrushao1994 commented on code in PR #11683:
URL: https://github.com/apache/tvm/pull/11683#discussion_r899908970
##########
python/tvm/meta_schedule/testing/distributed_measure_candidates.py:
##########
@@ -0,0 +1,199 @@
+# 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
+
+import argparse
+import glob
+import os
+import time
+
+from tqdm import tqdm # type: ignore
+from tvm import meta_schedule as ms
+from tvm.target import Target
+
+
+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.",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=128,
+ help="The batch size of candidates sent to builder and runner each time.",
+ )
+ return parser.parse_args()
+
+
+# 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
+ """
+ candidates, runner_results, build_fail_indices, run_fail_indices = [], [], [], []
+ build_time, run_time = 0.0, 0.0
+ context = ms.TuneContext(target=Target(args.target))
+ tuning_records = database.get_all_tuning_records()
+ for record in tuning_records:
+ candidates.append(context.get_measure_candidate(record.workload.mod, record.trace))
+ for idx in range(0, len(candidates), args.batch_size):
+ batch_candidates = candidates[idx : idx + args.batch_size]
+ context.set_measure_candidates(batch_candidates)
+ build_start_time = time.time()
+ context.send_to_builder(builder)
+ build_end_time = time.time()
+ context.send_to_runner(runner)
+ batch_runner_results = context.join()
+ run_end_time = time.time()
+ runner_results.extend(batch_runner_results)
+ for i, result in enumerate(context.builder_results):
+ if result.error_msg is None:
+ ms.utils.remove_build_dir(result.artifact_path)
+ else:
+ build_fail_indices.append(i + idx)
+ context.clear_measure_state()
+ build_time += build_end_time - build_start_time
+ run_time += run_end_time - build_end_time
+
+ 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 i, (record, result) in enumerate(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),
+ )
+ )
+ else:
+ run_fail_indices.append(i)
+ 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([str(n) for n in run_fail_indices]))
+ print(
+ f"Builder time: {build_time}, Runner time: {run_time}\n\
+ Failed number of builds: {len(build_fail_indices)},\
+ Failed number of runs: {len(run_fail_indices)}"
+ )
+
+
+args = _parse_args() # pylint: disable=invalid-name
+
+
+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,
Review Comment:
Usually we use `number=3`
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