leandron commented on a change in pull request #6302: URL: https://github.com/apache/incubator-tvm/pull/6302#discussion_r483632453
########## File path: python/tvm/driver/tvmc/compiler.py ########## @@ -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) + else: + with tvm.transform.PassContext(opt_level=3): + logging.debug("building relay graph (no tuning records provided)") + graph_module = relay.build(mod, tvm_target, params=params, target_host=tvm_target) + + # Generate output dump files with sources + dump_sources = dump_sources or [] + dumps = {} + for source_type in dump_sources: + lib = graph_module.get_lib() + # TODO lib.get_source call have inconsistent behavior for unsupported + # formats (@leandron). + source = str(mod) if source_type == "relay" else lib.get_source(source_type) + dumps[source_type] = source + + return graph_module.get_json(), graph_module.get_lib(), graph_module.get_params(), dumps + + +def save_module(module_path, graph, lib, params, cross=None): + """ + Create a tarball containing the generated TVM graph, + exported library and parameters + + Parameters + ---------- + module_path : str + path to the target tar.gz file to be created, + including the file name + 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. + cross : Union[str, Callable[[str, str, Optional[str]], None]] + Function that performs the actual compilation + + """ + lib_name = "mod.so" + graph_name = "mod.json" + param_name = "mod.params" + temp = util.tempdir() + path_lib = temp.relpath(lib_name) + if not cross: + logging.debug("exporting library to %s", path_lib) + lib.export_library(path_lib) + else: + logging.debug("exporting library to %s , using cross compiler %s", path_lib, cross) + lib.export_library(path_lib, cc.cross_compiler(cross)) + + with open(temp.relpath(graph_name), "w") as graph_file: + logging.debug("writing graph to file to %s", graph_file.name) + graph_file.write(graph) + + with open(temp.relpath(param_name), "wb") as params_file: + logging.debug("writing params to file to %s", params_file.name) + params_file.write(relay.save_param_dict(params)) + + logging.debug("saving module as tar file to %s", module_path) + with tarfile.open(module_path, "w") as tar: + tar.add(path_lib, lib_name) + tar.add(temp.relpath(graph_name), graph_name) + tar.add(temp.relpath(param_name), param_name) + + +def save_dumps(module_name, dumps, dump_root="."): + """ + Serialize dump files to the disk. + + Parameters + ---------- + module_name : list(Union[str, tvm.target.Target]) Review comment: It will be standardised on numpy style. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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