huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r698068143
########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,352 @@ +# 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. +"""Pipeline executor that executes pipeline containing TVM PackedFunc.""" +import json +import tvm._ffi +from tvm import relay +from tvm.contrib import graph_executor + + +def pipeline_executor_enabled(): + """check if pipeline executor enabled. + Return + ------ + enable: bool + return pipeline executor get enabled or not + """ + pipeline_enabled = False + try: + pipelinecreate = tvm._ffi.get_global_func("tvm.pipeline_executor.create") + assert pipelinecreate + pipeline_enabled = True + except ValueError: + print("pipeline executor not enabled!") + + return pipeline_enabled + + +def build_pipeline(mod_n_configs): + """build module list that can use for pipeline execution. + + Parameters + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline configuration + """ + mods = {} + config_len = len(mod_n_configs) + string_config = [{} for _ in range(config_len)] + for _, (ir_mod, mod_config) in enumerate(mod_n_configs.items()): + # init lib_name and json_name params with empty + lib_name = "" + json_name = "" + params_name = "" + # Get module configuration + assert "pipeline" in mod_config and "mod_indx" in mod_config["pipeline"] + # Get module index in pipeline configuration + mconf = mod_config["pipeline"].copy() + # Get mod device config + dev = mod_config["dev"] + mod_indx = mconf["mod_indx"] - 1 + target = mod_config["target"] + assert mod_indx < config_len + build_func = relay.build + # if there is a self defined build function then use it. + if "build" in mod_config and mod_config["build"]: + build_func = mod_config["build"] + + # build IRModule + mod = build_func( + ir_mod, + target, + params=mod_config["params"], + target_host=mod_config["target_host"], + mod_name=mod_config["mod_name"], + ) + + mconf["lib_name"] = lib_name + mconf["json_name"] = json_name + mconf["params_name"] = params_name + mconf["dev"] = "{},{}".format(dev.device_type, dev.device_id) + # Create pipeline configuration + string_config[mod_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return IRModule list and pipeline configuration + return mods, string_config + + +def create(pipeline_mods, mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + pipeline_mods : List[IRModule] + list of IRModule + + mod_config : Dict[int, Dict[str, Any]] + modules and modules dependency configuration informaiton. + + Returns + ------- + submodule : PipelineModule + Runtime pipeline module. + """ + + submodule = PipelineModule(pipeline_mods, mod_config) + return submodule + + +class PipelineModule(object): + """Wrapper runtime module. This is a thin wrapper of the underlying TVM module. + Parameters + ---------- + pipeline_mods : List[GraphModule] + The internal tvm module that holds the actual graph functions. + + pipeline_config : Dict[IRModule, Dict[str, Any]] + modules and modules dependency configuration informaiton. + + """ + + def __init__(self, pipeline_mods, pipeline_config): + self.pipeline_mods = pipeline_mods + self.mod_config = pipeline_config + mods, config = self.graph_executor_create(pipeline_mods, pipeline_config) + + pipelinecreate = tvm._ffi.get_global_func("tvm.pipeline_executor.create") + assert pipelinecreate + module = pipelinecreate(mods, config) + + self.module_ = module + + def graph_executor_create(self, pipeline_mods, mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + pipeline_mods : List[IRModule] + list of IRModule + + mod_config : Dict[int, Dict[str, Any]] + modules and modules dependency configuration informaiton. + + Returns + ------- + mods : GreaphModule + Runtime graph module. + """ + + mods = [] + for pipeline_mod in pipeline_mods: + mod = graph_executor.GraphModule( + pipeline_mod["default"](pipeline_mods[pipeline_mod]["dev"]) + ) + mods.append(mod.module) + + return mods, json.dumps(mod_config) + + +class PipelineModuleConfig: + """Pipeline Configuration Class, in this class there are 2 internal class, + first is Instance which use to represent Module, second is Interface which use + to represent Module input/output and Pipeline Module input/output, by setting + dependency relation between Interfaces this class can build the module + connection relation. + + The class Hierarchical as following. + PipelineModuleConfig ---> Pipe Instance ---> Interface(input/output) + ---> Module Instance ---> Interface(input/output) Review comment: put module into ModuleWrapper means here need a new List to replace dict for mod and pipe tracking, but that seems like not efficient when doing the the PipelineModuleConfig[mod1] search, if the purpose is to reduce dict number into 1 how about just make pipe and mod use same dict? -- This is an automated message from the Apache Git Service. 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