huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r704828587
########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,534 @@ +# 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 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 + ---------- + pipe_configs: PipelineConfig + build configuration informaton. + + Returns + ------- + ret: PipelineExecutorFactoryModule + the class that wrap module list and 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 + return PipelineExecutorFactoryModule(mods, string_config) + + +def create(pipe_executor_factory_module): + """Create a pipeline runtime executor. + + Parameters + ---------- + pipe_executor_factory_module : PipelineExecutorFactoryModule + Executor factory to storage IRModule list and pipeline configuration. + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_executor_factory_module) + + +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): + """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 ModuleWrapper: + """The class use use to represent Module and storage module idx and + Binding information. Review comment: fixed. -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
