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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/09/23 18:49:58 UTC
[GitHub] [incubator-tvm] comaniac commented on a change in pull request #6537: [tvmc] Introduce 'tune' subcommand (part 3/4)
comaniac commented on a change in pull request #6537:
URL: https://github.com/apache/incubator-tvm/pull/6537#discussion_r493803255
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
Review comment:
The current suggested numbers would be 0 for x86 and 1000 for other targets. We might need to reflect the best practice.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
Review comment:
Would be better to put it along with the hostname.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
Review comment:
remove this line.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
Review comment:
Looks like 0 is the only possible return value, since other cases will directly raise exceptions. If so, it seems not necessary to have a return value.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
+
+
+def get_tuning_tasks(mod, params, target, target_host=None, alter_layout=None):
+ """Get the tuning tasks for a given relay module.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module from which to extract tuning tasks.
+ params : dict
+ The params for the relay module.
+ target : tvm.target.Target
+ The compilation target.
+ target_host : str, optional
+ The compilation target for the host.
+ alter_layout : str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ tasks : list of autotvm.Tasks
+ list of tasks to be tuned
+
+ """
+ if not target_host:
+ target_host = target
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ tasks = autotvm.task.extract_from_program(
+ mod["main"],
+ target=target,
+ target_host=target_host,
+ params=params,
+ ops=(relay.op.get("nn.conv2d"),),
+ )
+
+ return tasks
+
+
+def tune_tasks(
+ tasks,
+ log_file,
+ measure_option,
+ tuner,
+ trials,
+ early_stopping=None,
+ tuning_records=None,
+):
+ """Tune a list of tasks and output the history to a log file.
+
+ Parameters
+ ----------
+ tasks : list
+ A list of autotvm.Tasks to tune.
+ log_file : str
+ A file to output the tuning history, in JSON.
+ measure_option : autotvm.measure_option
+ Options to build and run a tuning task.
+ tuner : str
+ Which tuner to use.
+ trials : int
+ The maximum number of tuning trials to perform.
+ early_stopping : int, optional
+ The minimum number of tuning trials to perform.
+ This will be equal to 'trials' if not specified.
+ tuning_records: str, optional
+ Path to the file produced by the tuning, to be used during
+ tuning.
+
+ """
+ if not tasks:
+ logging.warning("there were no tasks found to be tuned")
+ return
+
+ if not early_stopping:
+ early_stopping = trials
+
+ for i, tsk in enumerate(tasks):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+ # Create a tuner
+ if tuner in ("xgb", "xgb-rank"):
+ tuner_obj = XGBTuner(tsk, loss_type="rank")
+ elif tuner == "xgb_knob":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+ elif tuner == "ga":
+ tuner_obj = GATuner(tsk, pop_size=50)
+ elif tuner == "random":
+ tuner_obj = RandomTuner(tsk)
+ elif tuner == "gridsearch":
+ tuner_obj = GridSearchTuner(tsk)
+ else:
+ raise TVMCException("invalid tuner: %s " % tuner)
+
+ # If transfer learning is being used, load the existing results
+ if tuning_records and os.path.exists(tuning_records):
+ logging.warning("loading tuning records from %s", tuning_records)
+ start_time = time.time()
+ tuner_obj.load_history(autotvm.record.load_from_file(tuning_records))
+ logging.info("loaded history in %s", str(time.time() - start_time))
Review comment:
```suggestion
logging.info("loaded history in %.2f sec(s)", time.time() - start_time)
```
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
Review comment:
Build timeout, measurement timeout, or both?
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
Review comment:
Put this along with hostname and port. We can even consider creating an argument group for them.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
Review comment:
Explicitly say this is the error code.
##########
File path: python/tvm/driver/tvmc/common.py
##########
@@ -17,6 +17,11 @@
"""
Common utility functions shared by TVMC modules.
"""
+import logging
Review comment:
Use the standard logging, such as `logger = logging.getLogger("TVMC")`.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
Review comment:
We may need to figure out a better way to let user indicate whether to use RPC runner. Maybe we can use a single argument like
```
--rpc-runner=0.0.0.0:9099-rasp4b
```
@FrozenGene do you have an idea?
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
+
+
+def get_tuning_tasks(mod, params, target, target_host=None, alter_layout=None):
+ """Get the tuning tasks for a given relay module.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module from which to extract tuning tasks.
+ params : dict
+ The params for the relay module.
+ target : tvm.target.Target
+ The compilation target.
+ target_host : str, optional
+ The compilation target for the host.
+ alter_layout : str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ tasks : list of autotvm.Tasks
+ list of tasks to be tuned
+
+ """
+ if not target_host:
+ target_host = target
Review comment:
Is this correct? What if `target` is `cuda`?
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
+
+
+def get_tuning_tasks(mod, params, target, target_host=None, alter_layout=None):
+ """Get the tuning tasks for a given relay module.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module from which to extract tuning tasks.
+ params : dict
+ The params for the relay module.
+ target : tvm.target.Target
+ The compilation target.
+ target_host : str, optional
+ The compilation target for the host.
+ alter_layout : str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ tasks : list of autotvm.Tasks
+ list of tasks to be tuned
+
+ """
+ if not target_host:
+ target_host = target
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ tasks = autotvm.task.extract_from_program(
+ mod["main"],
+ target=target,
+ target_host=target_host,
+ params=params,
+ ops=(relay.op.get("nn.conv2d"),),
Review comment:
Why only extracting conv2d tasks?
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
+
+
+def get_tuning_tasks(mod, params, target, target_host=None, alter_layout=None):
+ """Get the tuning tasks for a given relay module.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module from which to extract tuning tasks.
+ params : dict
+ The params for the relay module.
+ target : tvm.target.Target
+ The compilation target.
+ target_host : str, optional
+ The compilation target for the host.
+ alter_layout : str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ tasks : list of autotvm.Tasks
+ list of tasks to be tuned
+
Review comment:
remove this line.
##########
File path: python/tvm/driver/tvmc/autotuner.py
##########
@@ -0,0 +1,301 @@
+# 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.
+"""
+Provides support to auto-tuning networks using AutoTVM.
+"""
+import os.path
+import logging
+import time
+
+from tvm import autotvm
+from tvm import relay
+from tvm.autotvm.tuner import GATuner
+from tvm.autotvm.tuner import GridSearchTuner
+from tvm.autotvm.tuner import RandomTuner
+from tvm.autotvm.tuner import XGBTuner
+
+from . import common, frontends
+from .common import TVMCException
+from .main import register_parser
+
+
+@register_parser
+def add_tune_parser(subparsers):
+ """ Include parser for 'tune' subcommand """
+
+ parser = subparsers.add_parser("tune", help="auto-tune a model")
+ parser.set_defaults(func=drive_tune)
+ parser.add_argument(
+ "--early-stopping",
+ type=int,
+ help="minimum number of trials before early stopping",
+ )
+ parser.add_argument("--hostname", help="hostname or IP address of the host machine")
+ parser.add_argument(
+ "--min-repeat-ms",
+ default=1000,
+ type=int,
+ help="minimum time to run each trial (in milliseconds)",
+ )
+ parser.add_argument(
+ "--model-format",
+ choices=frontends.get_frontend_names(),
+ help="specify input model format",
+ )
+ parser.add_argument(
+ "--number",
+ default=10,
+ type=int,
+ help="number of runs a single repeat is made of. "
+ "The final number of tuning executions is: "
+ "(1 + number * repeat)",
+ )
+ parser.add_argument(
+ "-o",
+ "--output",
+ required=True,
+ help="output file to store the tuning records for the tuning process",
+ )
+ parser.add_argument(
+ "--parallel",
+ default=4,
+ type=int,
+ help="the maximum number of parallel devices to use when tuning",
+ )
+ parser.add_argument("--port", default=9090, type=int, help="the port to connect to")
+ parser.add_argument(
+ "--repeat",
+ type=int,
+ default=1,
+ help="how many times to repeat each measurement",
+ )
+ parser.add_argument(
+ "--target",
+ help="compilation target as plain string, inline JSON or path to a JSON file",
+ required=True,
+ )
+ parser.add_argument("--timeout", default=10, help="time in seconds before timing out a config")
+ parser.add_argument("--tracker-key", help="the tracker key of the target device")
+ parser.add_argument(
+ "--trials",
+ type=int,
+ default=1000,
+ help="the maximum number of tuning trials to perform",
+ )
+ parser.add_argument(
+ "--tuner",
+ choices=["ga", "gridsearch", "random", "xgb", "xgb_knob", "xgb-rank"],
+ default="xgb",
+ help="type of tuner to use",
+ )
+ parser.add_argument(
+ "--tuning-records",
+ metavar="PATH",
+ help="path to an auto-tuning log file by AutoTVM.",
+ )
+ parser.add_argument(
+ "--desired-layout",
+ choices=["NCHW", "NHWC"],
+ default=None,
+ help="change the data layout of the whole graph",
+ )
+ # TODO (@leandron) This is a path to a physical file, but
+ # can be improved in future to add integration with a modelzoo
+ # or URL, for example.
+ parser.add_argument("FILE", help="path to the input model file")
+
+
+def drive_tune(args):
+ """Invoke auto-tuning with command line arguments
+
+ Parameters
+ ----------
+ args: argparse.Namespace
+ Arguments from command line parser.
+
+ Returns
+ --------
+ int
+ Zero if successfully completed
+
+ """
+
+ target = common.target_from_cli(args.target)
+ mod, params = frontends.load_model(args.FILE, args.model_format)
+
+ tasks = get_tuning_tasks(
+ mod=mod,
+ params=params,
+ target=target,
+ alter_layout=args.desired_layout,
+ )
+
+ if args.hostname:
+ logging.info("starting remote tuning:")
+ logging.info(" hostname: %s", args.hostname)
+ logging.info(" port : %s", args.port)
+ if not args.tracker_key:
+ raise common.TVMCException(
+ "need to provide tracker key (--tracker-key) for remote tuning"
+ )
+
+ runner = autotvm.RPCRunner(
+ key=args.tracker_key,
+ host=args.hostname,
+ port=args.port,
+ number=args.number,
+ repeat=args.repeat,
+ n_parallel=args.parallel,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+ else:
+ logging.info("starting localhost tuning")
+ runner = autotvm.LocalRunner(
+ number=args.number,
+ repeat=args.repeat,
+ timeout=args.timeout,
+ min_repeat_ms=args.min_repeat_ms,
+ )
+
+ tuning_option = {
+ "tuner": args.tuner,
+ "trials": args.trials,
+ "early_stopping": args.early_stopping,
+ "measure_option": autotvm.measure_option(
+ builder=autotvm.LocalBuilder(build_func="default"), runner=runner
+ ),
+ "tuning_records": args.tuning_records,
+ }
+ logging.debug(" tuning options: %s", tuning_option)
+
+ tune_tasks(tasks, args.output, **tuning_option)
+ return 0
+
+
+def get_tuning_tasks(mod, params, target, target_host=None, alter_layout=None):
+ """Get the tuning tasks for a given relay module.
+
+ Parameters
+ ----------
+ mod : tvm.relay.Module
+ The relay module from which to extract tuning tasks.
+ params : dict
+ The params for the relay module.
+ target : tvm.target.Target
+ The compilation target.
+ target_host : str, optional
+ The compilation target for the host.
+ alter_layout : str, optional
+ The layout to convert the graph to. Note, the convert layout
+ pass doesn't currently guarantee the whole of the graph will
+ be converted to the chosen layout.
+
+ Returns
+ -------
+ tasks : list of autotvm.Tasks
+ list of tasks to be tuned
+
+ """
+ if not target_host:
+ target_host = target
+
+ if alter_layout:
+ mod = common.convert_graph_layout(mod, alter_layout)
+
+ tasks = autotvm.task.extract_from_program(
+ mod["main"],
+ target=target,
+ target_host=target_host,
+ params=params,
+ ops=(relay.op.get("nn.conv2d"),),
+ )
+
+ return tasks
+
+
+def tune_tasks(
+ tasks,
+ log_file,
+ measure_option,
+ tuner,
+ trials,
+ early_stopping=None,
+ tuning_records=None,
+):
+ """Tune a list of tasks and output the history to a log file.
+
+ Parameters
+ ----------
+ tasks : list
+ A list of autotvm.Tasks to tune.
+ log_file : str
+ A file to output the tuning history, in JSON.
+ measure_option : autotvm.measure_option
+ Options to build and run a tuning task.
+ tuner : str
+ Which tuner to use.
+ trials : int
+ The maximum number of tuning trials to perform.
+ early_stopping : int, optional
+ The minimum number of tuning trials to perform.
+ This will be equal to 'trials' if not specified.
+ tuning_records: str, optional
+ Path to the file produced by the tuning, to be used during
+ tuning.
+
+ """
+ if not tasks:
+ logging.warning("there were no tasks found to be tuned")
+ return
+
+ if not early_stopping:
+ early_stopping = trials
+
+ for i, tsk in enumerate(tasks):
+ prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
+
+ # Create a tuner
+ if tuner in ("xgb", "xgb-rank"):
+ tuner_obj = XGBTuner(tsk, loss_type="rank")
+ elif tuner == "xgb_knob":
+ tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
+ elif tuner == "ga":
+ tuner_obj = GATuner(tsk, pop_size=50)
+ elif tuner == "random":
+ tuner_obj = RandomTuner(tsk)
+ elif tuner == "gridsearch":
+ tuner_obj = GridSearchTuner(tsk)
+ else:
+ raise TVMCException("invalid tuner: %s " % tuner)
+
+ # If transfer learning is being used, load the existing results
+ if tuning_records and os.path.exists(tuning_records):
+ logging.warning("loading tuning records from %s", tuning_records)
Review comment:
`info` should be a proper logging level here.
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