huajsj commented on a change in pull request #7892: URL: https://github.com/apache/tvm/pull/7892#discussion_r626214114
########## File path: tests/python/relay/test_analysis_pipeline.py ########## @@ -0,0 +1,144 @@ +# 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. + +import numpy as np +import tvm +import tvm.testing +from tvm import relay +from tvm.relay import transform +from tvm.contrib import graph_executor, pipeline_executor + + +def run_module(mod, dev, target, dname, data): + with tvm.transform.PassContext(opt_level=3): + lib = relay.build(mod, target) + + m = graph_executor.GraphModule(lib["default"](dev)) + m.set_input(dname, data) + m.run() + n = m.get_num_outputs() + output = m.get_output(0).asnumpy() + return output + + +def run_modules(mods, dev, target, dname, data): + for mod in mods: + data = run_module(mod, dev, target, dname, data) + + return data + + +def get_mannual_mod(): + mods = [] + dshape = (3, 3) + data = relay.var("data", relay.TensorType(dshape, "float32")) + mvalue1 = np.full((1), 5).astype("float32") + mvalue2 = np.full((1), 2).astype("float32") + mvalue3 = np.full((1), 3).astype("float32") + mvalue4 = np.full((1), 4).astype("float32") + mv1 = relay.Constant(tvm.nd.array(mvalue1)) + mv2 = relay.Constant(tvm.nd.array(mvalue2)) + mv3 = relay.Constant(tvm.nd.array(mvalue3)) + mv4 = relay.Constant(tvm.nd.array(mvalue4)) + net1 = relay.multiply(data, mv1) + + net2 = relay.add(data, mv2) + net2 = relay.add(net2, mv3) + + net3 = relay.multiply(data, mv4) + + net4 = relay.subtract(data, mv1) + + mods.append(tvm.IRModule.from_expr(relay.Function([data], net1))) + mods.append(tvm.IRModule.from_expr(relay.Function([data], net2))) + mods.append(tvm.IRModule.from_expr(relay.Function([data], net3))) + mods.append(tvm.IRModule.from_expr(relay.Function([data], net4))) + + return mods, dshape + + +""" +#split compute graph into 4 pipeline +""" +mods, dshape = get_mannual_mod() +""" +#Prepare batch data for pipeline feeding +""" +datas = [] +for i in range(len(mods) + 1): + datas.append(np.full(dshape, 3 + i).astype("float32")) + +""" +#Run with graph executor for verification purpose +""" +outs = [] +for data in datas: + outs.append(run_modules(mods, tvm.cpu(), "llvm", "data", data)) + +""" +#Parameter use for pipeline executor creation +""" +mod_config = {} +for i in range(len(mods)): + mconfig = {"target_host": None, "mod_name": "default", "build": None, "params": None} + # if cuda enabled, first 2 module us cuda as target + if i < 2 and tvm.testing.device_enabled("cuda"): + mconfig["target"] = "cuda" + mconfig["dev"] = tvm.gpu() + else: + mconfig["target"] = "llvm" + mconfig["dev"] = tvm.cpu() + + mod_config[mods[i]] = mconfig + +""" +#Build module and append module and device type into variable that +#use for pipeline creation. +#first and second pipeline use cuda when cuda enable, second and +#last pipeline use cpu +""" +with relay.build_config(opt_level=3): + pipeline_mod = tvm.relay.build(mods, config=mod_config) +""" +#Create pipeline executor +""" +pipeline_module = pipeline_executor.create(pipeline_mod) + +""" +#Use pipeline executor to pipeline the said pipeline which use different backend +""" +for data in datas: + pipeline_module.set_input("data", data) + pipeline_module.run() + +""" +Get result +""" +pipeline_outputs = [] +for i in range(len(datas)): + pipeline_outputs.append(pipeline_module.get_output()[0].asnumpy()) + +""" +#Stop pipeline execution. +""" +pipeline_module.stop() Review comment: automatically stop may have some side effect, first we need to set a timeout for no data checking , that means after all logic done without stop function, application can not immediately exit and still need wait for such timeout to quit. another thing is for implement automatically stop, we may need to add logic to automatically start logic into set_intput to make sure all pipeline executor can restart once have data, doing so may have big performance cost. -- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
