[MetaSchedule][Refactor] Introduce TuneConfig (apache#10986)

This PR unifies the existing `EvolutionarySearchConfig`, `ReplayFuncConfig` and `ReplayTraceConfig` into `TuneConfig`, and refactored the logic in `meta_schedule/tune.py`

python/tvm/meta_schedule/__init__.py

@@ -32,12 +32,5 @@
 from .extracted_task import ExtractedTask
 from .relay_integration import extract_task_from_relay
 from .search_strategy import MeasureCandidate
-from .tune import (
-    EvolutionarySearchConfig,
-    ReplayFuncConfig,
-    ReplayTraceConfig,
-    tune_relay,
-    tune_te,
-    tune_tir,
-)
+from .tune import TuneConfig, tune_relay, tune_te, tune_tir
 from .tune_context import TuneContext

python/tvm/meta_schedule/search_strategy/evolutionary_search.py

@@ -64,13 +64,13 @@ def __init__(
         *,
         num_trials_per_iter: int,
         max_trials_per_task: int,
-        population_size: int,
-        init_measured_ratio: float,
-        init_min_unmeasured: int,
-        genetic_num_iters: int,
-        genetic_mutate_prob: float,
-        genetic_max_fail_count: int,
-        eps_greedy: float,
+        population_size: int = 2048,
+        init_measured_ratio: float = 0.2,
+        init_min_unmeasured: int = 50,
+        genetic_num_iters: int = 4,
+        genetic_mutate_prob: float = 0.85,
+        genetic_max_fail_count: int = 10,
+        eps_greedy: float = 0.05,
     ) -> None:
         """Constructor"""
         self.__init_handle_by_constructor__(

python/tvm/meta_schedule/task_scheduler/round_robin.py

@@ -53,10 +53,12 @@ class RoundRobin(TaskScheduler):
     def __init__(
         self,
         tasks: List["TuneContext"],
+        task_weights: List[float],
         builder: Builder,
         runner: Runner,
         database: Database,
         max_trials: int,
+        *,
         cost_model: Optional[CostModel] = None,
         measure_callbacks: Optional[List[MeasureCallback]] = None,
     ) -> None:
@@ -66,6 +68,8 @@ def __init__(
         ----------
         tasks : List[TuneContext]
             List of tasks to schedule.
+        task_weights : List[float]
+            List of weights for each task. Not used in round robin.
         builder : Builder
             The builder.
         runner : Runner
@@ -79,6 +83,7 @@ def __init__(
         measure_callbacks: Optional[List[MeasureCallback]]
             The list of measure callbacks of the scheduler.
         """
+        del task_weights
         self.__init_handle_by_constructor__(
             _ffi_api.TaskSchedulerRoundRobin,  # type: ignore # pylint: disable=no-member
             tasks,

python/tvm/meta_schedule/testing/tune_relay_meta_schedule.py

@@ -18,15 +18,12 @@
 import argparse
 import json
 import logging
-import os
 
 import numpy as np  # type: ignore
 import tvm
 from tvm import meta_schedule as ms
-from tvm.ir.transform import PassContext
 from tvm.meta_schedule.testing.custom_builder_runner import run_module_via_rpc
 from tvm.meta_schedule.testing.relay_workload import get_network
-from tvm.relay import build as relay_build
 
 
 def _parse_args():
@@ -98,54 +95,6 @@ def _parse_args():
 ARGS = _parse_args()
 
 
-def tune_each_task(
-    mod,
-    target,
-    config,
-    runner,
-    work_dir,
-    params,
-):
-    extracted_tasks = ms.extract_task_from_relay(mod, target, params)
-    database = ms.database.JSONDatabase(
-        path_workload=os.path.join(work_dir, "default_database_workload.json"),
-        path_tuning_record=os.path.join(work_dir, "default_database_tuning_record.json"),
-    )
-    for task in extracted_tasks:
-        # pylint: disable=protected-access
-        tune_context = ms.tune.Parse._tune_context(
-            tune_context=None,
-            mod=ms.tune.Parse._mod(task.dispatched[0]),
-            target=target,
-            config=config,
-            task_name=task.task_name,
-            space_generator=None,
-            sch_rules=None,
-            postprocs=None,
-            mutator_probs=None,
-            num_threads=os.cpu_count(),
-        )
-        task_scheduler = ms.tune.Parse._task_scheduler(
-            None,
-            [tune_context],
-            task_weights=[1.0],
-            builder=ms.tune.Parse._builder(None),
-            runner=ms.tune.Parse._runner(runner),
-            database=database,
-            max_trials=config.max_trials_per_task,
-            cost_model=ms.tune.Parse._cost_model(None),
-            measure_callbacks=ms.tune.Parse._callbacks(None),
-        )
-        # pylint: enable=protected-access
-        task_scheduler.tune()
-    with target, ms.ApplyHistoryBest(database):
-        with PassContext(
-            opt_level=3,
-            config={"relay.backend.use_meta_schedule": True},
-        ):
-            return relay_build(mod, target=target, params=params)
-
-
 def main():
     mod, params, (input_name, input_shape, input_dtype) = get_network(
         ARGS.workload,
@@ -168,15 +117,14 @@ def main():
         alloc_repeat=alloc_repeat,
         max_workers=ARGS.rpc_workers,
     )
-    # lib = tune_each_task(
     lib = ms.tune_relay(
         mod=mod,
         target=ARGS.target,
-        config=ms.EvolutionarySearchConfig(
+        config=ms.TuneConfig(
+            strategy="evolutionary",
             num_trials_per_iter=64,
             max_trials_per_task=ARGS.num_trials,
             max_trials_global=ARGS.num_trials,
-            init_min_unmeasured=50,
         ),
         runner=runner,  # type: ignore
         work_dir=ARGS.work_dir,

python/tvm/meta_schedule/testing/tune_te_meta_schedule.py

@@ -100,11 +100,11 @@ def main():
     sch: Optional[tir.Schedule] = ms.tune_tir(
         mod=create_te_workload(ARGS.workload, 0),
         target=ARGS.target,
-        config=ms.EvolutionarySearchConfig(
+        config=ms.TuneConfig(
+            strategy="evolutionary",
             num_trials_per_iter=64,
             max_trials_per_task=ARGS.num_trials,
             max_trials_global=ARGS.num_trials,
-            init_min_unmeasured=50,
         ),
         runner=runner,  # type: ignore
         task_name=ARGS.workload,