huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r704946459
########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,533 @@ +# 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.relay.transform import InferType +from tvm.contrib import graph_executor + + +def pipeline_executor_enabled(): + """Check if pipeline executor get 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 IRModule with configurations of pipe_configs then + return Module list and Module dependency configuration. + + Parameters + ---------- + pipe_configs: PipelineConfig + build configuration informaton. + + Returns + ------- + ret: PipelineExecutorFactoryModule + the class that wrap module list and module dependency 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 mod_n_configs.items(): + mconf = mod_config["pipeline"].copy() + mod_idx = mconf["mod_idx"] - 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_idx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + return PipelineExecutorFactoryModule(mods, string_config) + + +def create(pipe_executor_factory_module): + """Create a pipeline runtime executor. + + Parameters + ---------- + pipe_executor_factory_module : PipelineExecutorFactoryModule + Executor factory with IRModule list and pipeline configuration. + + Returns + ------- + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_executor_factory_module) + + +class PipelineModule(object): + """Wrapper of runtime module. + + Parameters + ---------- + pipeline_config : Dict[GraphExecutorFactoryModule, 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" + pipeline_create = tvm._ffi.get_global_func( + "tvm.pipeline_executor.create", allow_missing=False + ) + assert pipeline_create + module = pipeline_create(mods, config) + + self.module_ = module + + def graph_executor_create(self, pipeline_mods, mod_config): + """Create graph_executor list and return text format configuration. + + Parameters + ---------- + pipeline_mods : List[GraphExecutorFactoryModule] + list of GraphExecutorFactoryModule + + mod_config : Dict[str, Any] + modules dependency configuration informaiton. + + Returns + ------- + mods : List[Module] + Module list. + + 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): + """The wrapper of each module to be pipelined. The wrapper mainly includes the + module itself as well as the binding that represents the connections of this + module's inputs and outputs to other modules. + """ + + class Binding: + """The class that use to storage module connection information. + The binding can be either "input" or "output". + + Parameters + ---------- + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like idx, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input it is string such as "data0"; + for output it is the idx integer such as 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """Get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_idx(self): + """Return owner idex if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.idx + + # If not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def is_global_interface(self): + """Check if this interface is global interface.""" + return not isinstance(self.io_owner, PipelineConfig.ModuleWrapper) + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def check_dag_acyclic(self, start, inputs): + """Check if the DAG that current binding stay is acircle""" Review comment: changed into "Here to check if the DAG that build by bindings connections is acyclic." -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
