added support for tuning microTVM models using meta_schedule.

include/tvm/meta_schedule/mutator.h

@@ -139,6 +139,8 @@ class Mutator : public runtime::ObjectRef {
   TVM_DLL static Map<Mutator, FloatImm, void> DefaultCUDATensorCore();
   /*! \brief Create default mutators for Hexagon */
   TVM_DLL static Map<Mutator, FloatImm, void> DefaultHexagon();
+  /*! \brief Create default mutators for Micro */
+  TVM_DLL static Map<Mutator, FloatImm, void> DefaultMicro();
 
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(Mutator, ObjectRef, MutatorNode);
 };

include/tvm/meta_schedule/postproc.h

@@ -165,6 +165,8 @@ class Postproc : public runtime::ObjectRef {
   TVM_DLL static Array<Postproc, void> DefaultCUDATensorCore();
   /*! \brief Create default postprocessors for Hexagon */
   TVM_DLL static Array<Postproc, void> DefaultHexagon();
+  /*! \brief Create default postprocessors for Micro */
+  TVM_DLL static Array<Postproc, void> DefaultMicro();
 
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(Postproc, ObjectRef, PostprocNode);
 };

include/tvm/meta_schedule/schedule_rule.h

@@ -298,6 +298,8 @@ class ScheduleRule : public runtime::ObjectRef {
   TVM_DLL static Array<ScheduleRule, void> DefaultCUDATensorCore();
   /*! \brief Create default schedule rules for Hexagon */
   TVM_DLL static Array<ScheduleRule, void> DefaultHexagon();
+  /*! \brief Create default schedule rules for Micro */
+  TVM_DLL static Array<ScheduleRule, void> DefaultMicro();
 
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(ScheduleRule, ObjectRef, ScheduleRuleNode);
 };

python/tvm/contrib/micro/meta_schedule/local_builder_micro.py

@@ -0,0 +1,84 @@
+# 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.
+"""Local builder for microTVM projects that compile on the local host"""
+
+import os
+import tempfile
+from typing import Optional, Dict
+from tvm.ir import IRModule
+from tvm.runtime import NDArray
+from tvm.target import Target
+from tvm.meta_schedule.builder import LocalBuilder
+from tvm.driver.build_module import OperatorModule
+from tvm import micro
+from tvm.contrib.tar import tar
+from tvm.relay.backend import Runtime
+from tvm.driver import build as tvm_build
+from tvm.tir.transform import RemoveWeightLayoutRewriteBlock
+
+
+def get_micro_local_builder():
+    """Return micro-compatible Builder for meta schedule."""
+
+    def _micro_build(
+        mod: IRModule, target: Target, _params: Optional[Dict[str, NDArray]]
+    ) -> OperatorModule:
+        """Build function for micro targets.
+
+        Parameters
+        ----------
+        mod : IRModule
+            The IRModule to be built.
+        target : Target
+            The target to be built.
+        _params : Optional[Dict[str, NDArray]]
+            The parameters to be used for the build. Must be None.
+
+        Returns
+        -------
+        rt_mod : OperatorModule
+            The built Module.
+        """
+
+        # Note: tvm_build assigns "global_symbol" to the name of generated C function
+        # changing it is necessary for micro targets,
+        # since the generated projects already include a main function.
+        prim_func = mod["main"].with_attr("global_symbol", "default_function")
+        mod = IRModule({"main": prim_func})
+        runtime = Runtime("crt", {"system-lib": True})
+        mod = RemoveWeightLayoutRewriteBlock(skip_ndarray_rewrite=True)(mod)
+        rt_mod = tvm_build(mod, target=target, runtime=runtime)
+        return rt_mod
+
+    def _micro_export(mod: OperatorModule) -> str:
+        """Export function for micro targets.
+
+        Parameters
+        ----------
+        mod : OperatorModule
+            The Module to be exported.
+
+        Returns
+        -------
+        artifact_path : str
+            The path to the exported Module.
+        """
+        artifact_path = os.path.join(tempfile.mkdtemp(), "tvm_tmp_mod." + tar.output_format)
+        micro.export_model_library_format(mod, artifact_path)
+        return artifact_path
+
+    return LocalBuilder(f_build=_micro_build, f_export=_micro_export)

python/tvm/contrib/micro/meta_schedule/rpc_runner_micro.py

@@ -0,0 +1,233 @@
+# 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.
+"""RPC Runner Micro"""
+
+from contextlib import contextmanager
+from typing import Callable, List, Optional
+from collections import namedtuple
+import signal
+
+from tvm import micro
+from tvm import nd
+from tvm.contrib.popen_pool import PopenPoolExecutor
+from tvm.rpc.server import Server
+from tvm.rpc.tracker import Tracker
+from tvm.meta_schedule.logging import get_logger
+from tvm.meta_schedule.utils import cpu_count, derived_object
+from tvm.meta_schedule.runner.config import EvaluatorConfig, RPCConfig
+from tvm.meta_schedule.runner import PyRunner, RunnerFuture, RunnerInput
+from tvm.meta_schedule.runner.rpc_runner import RPCRunnerFuture
+from tvm.meta_schedule.runner.utils import T_ARG_INFO_JSON_OBJ_LIST
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@derived_object
+class RPCRunnerMicro(PyRunner):
+    """RPC based runner for tuning micro models."""
+
+    def __init__(
+        self,
+        platform: str = "crt",
+        project_options: Optional[dict] = None,
+        rpc_config: Optional[RPCConfig] = None,
+        evaluator_config: Optional[EvaluatorConfig] = None,
+        max_workers: Optional[int] = None,
+        initializer: Optional[Callable[[], None]] = None,
+    ) -> None:
+        """Constructor
+
+        Parameters
+        ----------
+        platform: str
+            The platform used for project generation.
+        project_options: dict
+            The options for the generated micro project.
+        rpc_config: RPCConfig
+            The rpc configuration.
+        evaluator_config: EvaluatorConfig
+            The evaluator configuration.
+        max_workers: Optional[int] = None
+            The maximum number of connections. Defaults to number of logical CPU cores.
+        initializer: Optional[Callable[[], None]]
+            The initializer function.
+        """
+        super().__init__()
+        self.platform = platform
+        if project_options is None:
+            project_options = {}
+        self.project_options = project_options
+        self.rpc_config = RPCConfig._normalized(rpc_config)
+        self.evaluator_config = EvaluatorConfig._normalized(evaluator_config)
+
+        if max_workers is None:
+            max_workers = cpu_count(logical=True)
+        logger.info("RPCRunner: max_workers = %d", max_workers)
+        self.pool = PopenPoolExecutor(
+            max_workers=max_workers,
+            timeout=rpc_config.session_timeout_sec,
+            initializer=initializer,
+        )
+
+    def run(self, runner_inputs: List[RunnerInput]) -> List[RunnerFuture]:
+        results: List[RunnerFuture] = []
+
+        for runner_input in runner_inputs:
+            future = RPCRunnerFuture(
+                future=self.pool.submit(
+                    _worker_func,
+                    self.platform,
+                    self.project_options or {},
+                    self.rpc_config,
+                    self.evaluator_config,
+                    str(runner_input.artifact_path),
+                    str(runner_input.device_type),
+                    tuple(arg_info.as_json() for arg_info in runner_input.args_info),
+                ),
+                timeout_sec=self.rpc_config.session_timeout_sec,
+            )
+            results.append(future)  # type: ignore
+        return results
+
+
+def _worker_func(
+    platform: str,
+    project_options: dict,
+    rpc_config: RPCConfig,
+    evaluator_config: EvaluatorConfig,
+    artifact_path: str,
+    device_type: str,
+    args_info: T_ARG_INFO_JSON_OBJ_LIST,
+) -> List[float]:
+
+    module_loader = micro.AutoTvmModuleLoader(
+        template_project_dir=micro.get_microtvm_template_projects(platform),
+        project_options=project_options,
+    )
+
+    remote_kw = {
+        "device_key": rpc_config.tracker_key,
+        "host": rpc_config.tracker_host,
+        "port": rpc_config.tracker_port,
+        "priority": 0,
+        "timeout": 100,
+    }
+    build_result = namedtuple("BuildResult", ["filename"])(artifact_path)
+
+    with module_loader(remote_kw, build_result) as (remote, mod):
+        dev = remote.device(device_type, 0)
+        f_prepare = ""
+        if evaluator_config.enable_cpu_cache_flush:
+            f_prepare = "cache_flush_cpu_non_first_arg"
+        time_f = mod.time_evaluator(
+            mod.entry_name,
+            dev,
+            number=evaluator_config.number,
+            repeat=evaluator_config.repeat,
+            min_repeat_ms=evaluator_config.min_repeat_ms,
+            f_preproc=f_prepare,
+        )
+
+        random_fill = remote.get_function("tvm.contrib.random.random_fill")
+        args = [nd.empty(x[2], x[1], dev) for x in args_info]
+        for arg in args:
+            random_fill(arg)
+        dev.sync()
+
+        costs = time_f(*args).results
+    return costs
+
+
+@contextmanager
+def get_rpc_runner_micro(
+    platform,
+    options,
+    rpc_config: RPCConfig = None,
+    evaluator_config: EvaluatorConfig = None,
+    session_timeout_sec=300,
+):
+    """Parameters
+    ----------
+    platform: str
+        The platform used for project generation.
+    project_options: dict
+        The options for the generated micro project.
+    rpc_config: RPCConfig
+        The rpc configuration.
+    evaluator_config: EvaluatorConfig
+        The evaluator configuration.
+    session_timeout_sec: int
+        The session timeout. if the number of candidates sent to runner is larger
+        than the runner workers, increase the timeout.
+    """
+    if rpc_config is None:
+        tracker_host = "127.0.0.1"
+        tracker_port = 9000
+        tracker_key = "$local$device$%d" % tracker_port
+        rpc_config = RPCConfig(
+            tracker_host=tracker_host,
+            tracker_port=tracker_port,
+            tracker_key=tracker_key,
+            session_priority=0,
+            session_timeout_sec=session_timeout_sec,
+        )
+    tracker_port_end = rpc_config.tracker_port + 1000
+
+    if evaluator_config is None:
+        evaluator_config = EvaluatorConfig(
+            number=3,
+            repeat=1,
+            min_repeat_ms=100,
+            enable_cpu_cache_flush=False,
+        )
+
+    tracker = Tracker(
+        port=rpc_config.tracker_port,
+        port_end=tracker_port_end,
+        silent=True,
+        reuse_addr=True,
+        timeout=60,
+    )
+    server = Server(
+        port=rpc_config.tracker_port,
+        port_end=tracker_port_end,
+        key=rpc_config.tracker_key,
+        silent=True,
+        tracker_addr=(rpc_config.tracker_host, rpc_config.tracker_port),
+        reuse_addr=True,
+        timeout=60,
+    )
+
+    def terminate():
+        tracker.terminate()
+        server.terminate()
+
+    def handle_SIGINT(signal, frame):
+        terminate()
+        raise KeyboardInterrupt("Received SIGINT")
+
+    signal.signal(signal.SIGINT, handle_SIGINT)
+
+    try:
+        yield RPCRunnerMicro(
+            platform=platform,
+            project_options=options,
+            rpc_config=rpc_config,
+            evaluator_config=evaluator_config,
+        )
+    finally:
+        terminate()