[GitHub] [incubator-tvm] leandron commented on a change in pull request #6302: [tvmc] command line driver 'compile' (part 2/4)

[GitBox <gi...@apache.org>]

leandron commented on a change in pull request #6302:
URL: https://github.com/apache/incubator-tvm/pull/6302#discussion_r482832429



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File path: python/tvm/driver/tvmc/compiler.py
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@@ -0,0 +1,305 @@
+# 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 compile networks both AOT and JIT.
+"""
+import logging
+import os.path
+import tarfile
+from pathlib import Path
+
+import tvm
+from tvm import autotvm
+from tvm import relay
+from tvm.contrib import cc
+from tvm.contrib import util
+
+from . import common, frontends
+from .main import register_parser
+
+
+@register_parser
+def add_compile_parser(subparsers):
+    """ Include parser for 'compile' subcommand """
+
+    parser = subparsers.add_parser("compile", help="compile a model")
+    parser.set_defaults(func=drive_compile)
+    parser.add_argument(
+        "--cross-compiler",
+        default="",
+        help="the cross compiler to generate target libraries, e.g. 'aarch64-linux-gnu-gcc'",
+    )
+    parser.add_argument(
+        "--dump-code",
+        metavar="FORMAT",
+        default="",
+        help="comma separarated list of formats to export, e.g. 'asm,ll,relay' "
+    )
+    parser.add_argument(
+        "--model-format",
+        choices=frontends.get_frontends(),
+        help="specify input model format",
+    )
+    parser.add_argument(
+        "--input-shape",
+        type=common.parse_input_shapes,
+        metavar="INPUT_SHAPE,[INPUT_SHAPE]...",
+        help="for pytorch, e.g. '(1,3,224,224)'",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        default="module.tar",
+        help="output the compiled module to an archive",
+    )
+    parser.add_argument(
+        "--target",
+        help="compilation target as plain string, inline JSON or path to a JSON file",
+        required=True
+    )
+    parser.add_argument(
+        "--tuning-records",
+        metavar="PATH",
+        default="",
+        help="path to an auto-tuning log file from AutoTVM"
+    )
+    parser.add_argument(
+        "--desired-layout",
+        choices=["NCHW", "NHWC"],
+        default=None,
+        help="change the data layout of the whole graph",
+    )
+    parser.add_argument(
+        "-v", "--verbose", action="count", default=0, help="increase verbosity"
+    )
+    parser.add_argument("FILE")
+
+
+def drive_compile(args):
+    """ Invoke tvmc.compiler module with command line arguments """
+
+    graph, lib, params, dumps = compile_model(
+        args.FILE,
+        args.target,
+        args.dump_code,
+        "",
+        args.model_format,
+        args.input_shape,
+        args.tuning_records,
+        args.tensor_layout,
+    )
+
+    if dumps:
+        save_dumps(args.output, dumps)
+
+    save_module(args.output, graph, lib, params, args.cross_compiler)
+    return 0
+
+
+def compile_model(
+        path,
+        target,
+        dump_sources=None,
+        target_host=None,
+        model_format=None,
+        shapes=None,
+        tuning_records=None,
+        alter_layout=None,
+):
+    """Compile a model from a supported framework into a TVM module.
+
+    This function takes a union of the arguments of both frontends.load_model
+    and compiler.compile_relay. The resulting TVM module can be executed using
+    the graph runtime.
+
+    Returns
+    -------
+    graph : str
+        A JSON-serialized TVM execution graph.
+    lib : tvm.module.Module
+        A TVM module containing the compiled functions.
+    params : dict
+        The parameters (weights) for the TVM module.
+    dumps : dict
+            Dictionary containing the dumps specified.
+
+    """
+    dump_sources = [x.strip() for x in  dump_sources.split(',')] if dump_sources else None
+    mod, params = frontends.load_model(path, model_format, shapes)
+
+    return compile_relay(
+        mod,
+        params,
+        target,
+        dump_sources=dump_sources,
+        target_host=target_host,
+        tuning_records=tuning_records,
+        alter_layout=alter_layout,
+    )
+
+
+def compile_relay(
+        mod,
+        params,
+        target,
+        dump_sources=None,
+        target_host=None,
+        tuning_records=None,
+        alter_layout=None,
+):
+    """Compile a relay module to a TVM module for the graph runtime.
+
+    Parameters
+    ----------
+    mod : tvm.relay.Module
+        The relay module to compile.
+    params : dict
+        The parameters (weights) for the relay module.
+    target : str
+        The target for which to compile. Can be a plain string or
+        a path.
+    dump_sources : list, optional
+        Dump the generated code for the specified source types, on
+        the requested target.
+    target_host : Union[str, tvm.target.Target], optional
+        The target of the host machine if host-side code
+        needs to be generated.
+    tuning_records: str, optional
+        Name of the file produced by the tuning to be used during
+        compilation.
+    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
+    -------
+    graph : str
+        A JSON-serialized TVM execution graph.
+    lib : tvm.module.Module
+        A TVM module containing the compiled functions.
+    params : dict
+        The parameters (weights) for the TVM module.
+    dumps : dict
+        Dictionary containing the dumps specified.
+
+    """
+
+    if alter_layout:
+        mod = common.convert_graph_layout(mod, alter_layout)
+
+    if os.path.exists(str(target)):
+        with open(target) as target_file:
+            logging.info("using target input from file: %s", target)
+            target = "".join(target_file.readlines())
+
+    # TODO: We don't have an API to collect a list of supported
+    #       targets yet. (@leandron)
+    logging.debug("creating target from input: %s", target)
+    tvm_target = tvm.target.create(target)
+    target_host = target_host or ""
+
+    if tuning_records:
+        logging.debug("tuning records file provided: %s", tuning_records)
+        with autotvm.apply_history_best(tuning_records):
+            with tvm.transform.PassContext(opt_level=3):
+                logging.debug("building relay graph with tuning records")
+                graph_module = relay.build(mod, tvm_target, params=params, target_host=tvm_target)

Review comment:
       It will be introduce in the next PR, related to `tune` subcommand, in parallel with the `run` PR.
   
   I added a TODO.




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