huajsj commented on a change in pull request #14: URL: https://github.com/apache/tvm-rfcs/pull/14#discussion_r690031140
########## File path: rfcs/0012-pipeline-executor.md ########## @@ -0,0 +1,214 @@ +<!--- 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. --> +- Feature Name: Pipeline Executor +- Start Date: 2021-07-30 +- RFC PR: [apache/tvm-rfcs#0014](https://github.com/apache/tvm-rfcs/pull/0014) +- GitHub Issue: [apache/tvm#8596](https://github.com/apache/tvm/issues/8596) + +## 1. Summary + + +This proposal introduces Pipeline Executor: A runtime executor that by scheduling +splitted subgraph of relay graph in pipeline to implement task level parallism to +improve compute throughput. + +## 2. Motivation + + + +Currently more and more edge device inference deployments happen on SOC devices. +Since SOC devices have heterogeneous chipset like GPU, FPGA, CPU, DSP, etc. To reach the best +performance, there is a requirement to run an ML network in these heterogeneous chipsets. +However, currently graph executor does not have parallelism logic, and the existing data parallelism +solution only supports parallel on homogeneous chipset(device). Then, the only way to do batch processing +on heterogeneous devices with TVM is to treat a whole ML network as a schedule unit and run it on +different heterogeneous devices, but that would cause latency issue (low speed chipset becomes the +latency bottleneck for single data processing). + +Therefore, we need a runtime executor that can provide parallel scheduling functionality +with a finer-grained schedule unit like subgraph (a group of operator with dependency relation) +to be more efficient to use SOC heterogeneous hardware resource to achieve a better performance. + + +### Benefits of Pipeline Executor + +There are three benefits for Pipeline Executor + +Pipeline Executor provides: +* Compute a single network on multiple backends in parallel to improve performance. + +* Use RPC to perform distributed computation cross multiple remote devices. + +* Pipeline executor provide the capability to integrate non-DNN model function. + +## 3. Guide-level explanation +Pipeline Executor is a runtime executor which implements pipeline execution logic for multiple +subgraphs and relies on graph_executor for operator storage and execution. + +This section introduces the use case for Pipeline Executor. + +* 1. Using Automatic Graph Split feature to construct pipeline subgraph and configuration. +* 2. Use pipeline_executor to build a pipeline module with the subgraphs and configuration. +* 3. Use pipeline_executor to load the pipeline module to run network in pipeline parallelism mode. + +### 3.1. Using Automatic Graph Split feature to construct pipeline subgraph and configuration. 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]
