huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r697923152
########## File path: tests/python/relay/test_pipeline_executor.py ########## @@ -0,0 +1,256 @@ +# 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 get_mannual_mod(): + """ + # get list of module that represent a subgraph + """ + mods = [] + dshape = (3, 3) + data = relay.var("data_0", relay.TensorType(dshape, "float32")) + data21 = relay.var("data_1", relay.TensorType(dshape, "float32")) + data_net1_output_1 = relay.var("data_0", relay.TensorType(dshape, "float32")) + data_net1_output_2 = relay.var("data_1", relay.TensorType(dshape, "float32")) + data_net2_output_1 = relay.var("data_0", relay.TensorType(dshape, "float32")) + mvalue1 = np.full((1), 1).astype("float32") + mvalue2 = np.full((1), 2).astype("float32") + mvalue3 = np.full((1), 3).astype("float32") + mv1 = relay.Constant(tvm.nd.array(mvalue1)) + mv2 = relay.Constant(tvm.nd.array(mvalue2)) + mv3 = relay.Constant(tvm.nd.array(mvalue3)) + + """ + # net1 have three output, output3 is final output. + """ + + net_output1 = relay.add(data, mv1) + net_output2 = relay.subtract(data, mv2) + net_output3 = relay.multiply(data, mv3) + + """ + # net2 use net1 output1 as input. + """ + net2 = relay.add(data_net1_output_1, mv2) + net2 = relay.add(net2, data21) + net2 = relay.add(net2, mv3) + + """ + # net3 use net2 output1 and net1 outpu2 as input. + """ + net3 = relay.multiply(data_net2_output_1, mv3) + net3 = relay.add(net3, data_net1_output_2) + + mods.append( + tvm.IRModule.from_expr( + relay.Function([data], relay.Tuple([net_output1, net_output2, net_output3])) + ) + ) + mods.append(tvm.IRModule.from_expr(relay.Function([data_net1_output_1, data21], net2))) + mods.append( + tvm.IRModule.from_expr(relay.Function([data_net1_output_2, data_net2_output_1], net3)) + ) + + return mods, dshape + + +def get_manual_conf(mods): + """ + # This function use to generate manual pipe line configueration, + # the result use to verify if the pipe configuration can generate + # correct result. + """ + mod_config = {} + """ + # set configure + """ + mconfig1 = {} + """ + # third output is final output, second output for mod3, first for mod2 + # input + """ + mconfig1["pipeline"] = { + "mod_indx": 1, + "output": [ + {"output_indx": 0, "dependent": [{"mod_indx": 2, "input_name": "data_0"}]}, + {"output_indx": 1, "dependent": [{"mod_indx": 3, "input_name": "data_0"}]}, + {"output_indx": 2, "dependent": [{"mod_indx": 0, "input_name": "0"}]}, + ], + } + mod_config[mods[0]] = mconfig1 + + mconfig2 = {} + mconfig2["pipeline"] = { + "mod_indx": 2, + "output": [ + {"output_indx": 0, "dependent": [{"mod_indx": 3, "input_name": "data_1"}]}, + ], + } + mod_config[mods[1]] = mconfig2 + + mconfig3 = {} + + mconfig3["pipeline"] = { + "mod_indx": 3, + "output": [{"output_indx": 0, "dependent": [{"mod_indx": 0, "input_name": "1"}]}], + } + mod_config[mods[2]] = mconfig3 + return mod_config + + +def pipeline_module_create(target): + """ + #Get 3 pipeline module. + """ + (mod1, mod2, mod3), dshape = get_mannual_mod() + + # Prepare batch data for pipeline feeding + datas = [] + for i in range(5): + datas.append(np.full(dshape, 3 + i).astype("float32")) + + pipe_config = pipeline_executor.PipelineModuleConfig([mod1, mod2, mod3]) + + # Create pipeline compute input/output and subgraph dependent relation. + + # pipeline compute input "data_0" would get forward to mod1 as input "data_0" + pipe_config.connect(pipe_config.pipe_input("data_0"), pipe_config[mod1].input("data_0")) + + # pipeline compute input "data_1" would get forward to mod2 as input "data_1" + pipe_config.connect(pipe_config.pipe_input("data_1"), pipe_config[mod2].input("data_1")) + + # mod1 output(0) would get forward to mod2 as input "data_0" + pipe_config.connect(pipe_config[mod1].output(0), pipe_config[mod2].input("data_0")) + + # mod1 output(1) would get forward to mod3 as input "data_0" + pipe_config.connect(pipe_config[mod1].output(1), pipe_config[mod3].input("data_0")) + + # mod2 output(0) would get forward to mod3 as input "data_1" + pipe_config.connect(pipe_config[mod2].output(0), pipe_config[mod3].input("data_1")) + + # mod1 output(2) would get forward as final pipeline compute output(1) + pipe_config.connect(pipe_config[mod1].output(2), pipe_config.pipe_output("0")) + + # mod3 output(0) would get forward as final pipeline compute output(2) + pipe_config.connect(pipe_config[mod3].output(0), pipe_config.pipe_output("1")) + """ + # print configueration, the expect result like following. + # + #Inputs + # |data_0: mod1:data_0 + # |data_1: mod2:data_1 + # + #output + # |output(1) : mod1.output(2) + # |output(2) : mod3.output(0) + # + #connections + # |mod1.output(0)-> mod2.data_0 + # |mod1.output(1)-> mod3.data_0 + # |mod2.output(0)-> mod3.data_1 + """ + + print(pipe_config) + + """ + # connection correctness veify + """ + try: + pipe_config.connect(pipe_config[mod2].output(0), pipe_config[mod1].input("data_0")) + assert 0, f"wrong module connect order check not pass!" + pipe_config.connect(pipe_config.pipe_input("data_0"), pipe_config[mod1].output(0)) + assert 0, f"wrong global input connect check not pass!" + except: + print("connection correctness check pass") Review comment: fixed. -- This is an automated message from the Apache Git Service. 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