[TVMC] Split common tvmc file into more specific files

python/tvm/driver/tvmc/__init__.py

@@ -14,11 +14,20 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
-# pylint: disable=redefined-builtin
+# pylint: disable=redefined-builtin,wrong-import-position
 """
 TVMC - TVM driver command-line interface
 """
 
+
+class TVMCException(Exception):
+    """TVMC Exception"""
+
+
+class TVMCImportError(TVMCException):
+    """TVMC TVMCImportError"""
+
+
 from . import micro
 from . import runner
 from . import autotuner

python/tvm/driver/tvmc/arguments.py

@@ -0,0 +1,52 @@
+# 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.
+"""
+TVMC Argument Parsing
+"""
+
+import argparse
+
+from tvm.driver.tvmc import TVMCException
+
+
+class TVMCSuppressedArgumentParser(argparse.ArgumentParser):
+    """
+    A silent ArgumentParser class.
+    This class is meant to be used as a helper for creating dynamic parsers in
+    TVMC. It will create a "supressed" parser based on an existing one (parent)
+    which does not include a help message, does not print a usage message (even
+    when -h or --help is passed) and does not exit on invalid choice parse
+    errors but rather throws a TVMCException so it can be handled and the
+    dynamic parser construction is not interrupted prematurely.
+    """
+
+    def __init__(self, parent, **kwargs):
+        # Don't add '-h' or '--help' options to the newly created parser. Don't print usage message.
+        # 'add_help=False' won't supress existing '-h' and '--help' options from the parser (and its
+        # subparsers) present in 'parent'. However that class is meant to be used with the main
+        # parser, which is created with `add_help=False` - the help is added only later. Hence it
+        # the newly created parser won't have help options added in its (main) root parser. The
+        # subparsers in the main parser will eventually have help activated, which is enough for its
+        # use in TVMC.
+        super().__init__(parents=[parent], add_help=False, usage=argparse.SUPPRESS, **kwargs)
+
+    def exit(self, status=0, message=None):
+        # Don't exit on error when parsing the command line.
+        # This won't catch all the errors generated when parsing tho. For instance, it won't catch
+        # errors due to missing required arguments. But this will catch "error: invalid choice",
+        # which is what it's necessary for its use in TVMC.
+        raise TVMCException()

python/tvm/driver/tvmc/autotuner.py

@@ -34,11 +34,12 @@
 from tvm.autotvm.tuner import XGBTuner
 from tvm.target import Target
 
-from . import common, composite_target, frontends
-from .common import TVMCException
+from . import TVMCException, composite_target, frontends
 from .main import register_parser
 from .model import TVMCModel
-from .target import generate_target_args, reconstruct_target_args
+from .target import target_from_cli, generate_target_args, reconstruct_target_args
+from .shape_parser import parse_shape_string
+from .transform import convert_graph_layout
 
 
 # pylint: disable=invalid-name
@@ -220,7 +221,7 @@ def add_tune_parser(subparsers, _):
         "--input-shapes",
         help="specify non-generic shapes for model to run, format is "
         '"input_name:[dim1,dim2,...,dimn] input_name2:[dim1,dim2]"',
-        type=common.parse_shape_string,
+        type=parse_shape_string,
     )
 
 
@@ -256,9 +257,7 @@ def drive_tune(args):
         logger.info("RPC tracker port: %s", rpc_port)
 
         if not args.rpc_key:
-            raise common.TVMCException(
-                "need to provide an RPC tracker key (--rpc-key) for remote tuning"
-            )
+            raise TVMCException("need to provide an RPC tracker key (--rpc-key) for remote tuning")
     else:
         rpc_hostname = None
         rpc_port = None
@@ -376,7 +375,7 @@ def tune_model(
     tuning_records : str
         The path to the produced tuning log file.
     """
-    target, extra_targets = common.target_from_cli(target, additional_target_options)
+    target, extra_targets = target_from_cli(target, additional_target_options)
     target, target_host = Target.check_and_update_host_consist(target, target_host)
     # TODO(jwfromm) Remove this deepcopy once AlterOpLayout bug that mutates source
     # model is fixed. For now, creating a clone avoids the issue.
@@ -399,7 +398,7 @@ def tune_model(
 
     if rpc_key:
         if hostname is None or port is None:
-            raise common.TVMCException(
+            raise TVMCException(
                 "You must provide a hostname and port to connect to a remote RPC device."
             )
         if isinstance(port, str):
@@ -520,7 +519,7 @@ def autotvm_get_tuning_tasks(
     target, target_host = Target.check_and_update_host_consist(target, target_host)
 
     if alter_layout:
-        mod = common.convert_graph_layout(mod, alter_layout)
+        mod = convert_graph_layout(mod, alter_layout)
 
     tasks = autotvm.task.extract_from_program(
         mod["main"],
@@ -569,7 +568,7 @@ def autoscheduler_get_tuning_tasks(
     target, target_host = Target.check_and_update_host_consist(target, target_host)
 
     if alter_layout:
-        mod = common.convert_graph_layout(mod, alter_layout)
+        mod = convert_graph_layout(mod, alter_layout)
 
     # Extract the tasks
     tasks, task_weights = auto_scheduler.extract_tasks(