huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r698896263
########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,395 @@ +# 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 a series of modules in a pipeline fashion.""" +import json +import tvm._ffi +from tvm import relay +from tvm.contrib import graph_executor + + +def pipeline_executor_enabled(): + """check if pipeline executor is enabled. + + Return + ------- + enable: bool + Return pipeline executor is enabled or not. + """ + return tvm._ffi.get_global_func("tvm.pipeline_executor.create", + allow_missing=True) is not None + + +def build(pipe_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 = {} + mod_n_configs = pipe_configs.get_config() + config_len = len(mod_n_configs) + string_config = [{} for _ in range(config_len)] + #for _, (ir_mod, mod_config) in enumerate(mod_n_configs.items()): + for ir_mod, mod_config in mod_n_configs.items(): + mconf = mod_config["pipeline"].copy() + mod_indx = mconf["mod_indx"] - 1 + # Get mod device config + dev = mod_config["dev"] + target = mod_config["target"] + 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["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 PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + + Returns + ------- + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + +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, pipe_mod_config): + self.pipeline_mods_ = pipe_mod_config.pipeline_mods_ + self.mod_config_ = pipe_mod_config.mods_config_ + mods, config = self.graph_executor_create(self.pipeline_mods_, self.mod_config_) + assert pipeline_executor_enabled(), \ + "Pipeline executor is not enabled. Please \ + re-build TVM with USE_PIPELINE_EXECUTOR=ON" + pipelinecreate = tvm._ffi.get_global_func("tvm.pipeline_executor.create", + allow_missing=False) + assert pipelinecreate + module = pipelinecreate(mods, config) + + self.module_ = module + + def graph_executor_create(self, pipeline_mods, mod_config): + """Create graph_executor list and return string format config. + + Parameters + ---------- + + pipeline_mods : List[IRModule] + list of IRModule + + mod_config : Dict[int, Dict[str, Any]] + modules and modules dependency configuration informaiton. + + Returns + ------- + mods : List[GraphModule] + Runtime graph module. + + mod_config : str + mods configuration + """ + + 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 PipelineConfig(object): + """Pipeline Configuration Class, in this class there are 2 internal class, + first is Module 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. + PipelineConfig ---> ModuleWrapper ---> Interface(input/output) + """ + + class ModuleWrapper: + """The class use use to represent Module and storage module index and + Interface information. + """ + + class Interface: + """The class that use to storage module connection information. + There are 2 types Interface Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + itype : integer + Interface type, 1 is input interface, 2 is output interface + + name : str/integer + Interface name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, itype, name): + self.owner_ = owner + self.itype_ = itype + self.name_ = str(name) + self.dependent_ = [] + + def get_name(self): + mname = "" + if self.owner_: + mname = self.owner_.name_ + + return mname, self.name_ + + def get_owner_indx(self): + return self.owner_.indx_ + + def get_dependent_str(self): + name = "" + for dependent in self.dependent_: + mname, dname = dependent.get_name() + name = name + (mname + ":output(" + dname if self.itype_ == 2 else "") + name = name + (")" if self.itype_ == 2 else mname + ":" + dname) + return name + + def add_dependent(self, dependent): + """ + # check if the dependency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to next module input + # 3. module output to global output + """ + owner_indx = self.get_owner_indx() + dep_owner_indx = dependent.get_owner_indx() + assert owner_indx != dep_owner_indx, f"can not set self as dependent." + assert not ( + owner_indx > dep_owner_indx + and not (dependent.itype_ == 2 and dep_owner_indx == 0) + ), f"dependent only can be next module interface or global output." + assert not ( + owner_indx == 0 and dependent.itype_ != 1 + ), f"global input only can set dependent with module input." + + self.dependent_.append(dependent) + + def __init__(self, indx=0): + self.indx_ = indx + self.name_ = "mod" + str(indx) if indx else "" Review comment: indx 0 reserved for the ModuleWraper class that present pipelinemodule, so should not have mod0. -- This is an automated message from the Apache Git Service. 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