huajsj commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r707824767
########## File path: tests/python/relay/test_pipeline_executor.py ########## @@ -0,0 +1,251 @@ +# 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 pytest +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)) + + """The first model has three output.""" + + net1_output1 = relay.add(data, mv1) + net1_output2 = relay.subtract(data, mv2) + net1_output3 = relay.multiply(data, mv3) + + """The second model use first model net1_output1 as first input, + here data_net1_output_1 represent net_output1, the second input + of this model is data21. + """ + net2 = relay.add(data_net1_output_1, mv2) + net2 = relay.add(net2, data21) + net2_output = relay.add(net2, mv3) + + """The third model use the second model net2_output as first input + and use the first model net1_output2 as second 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([net1_output1, net1_output2, net1_output3])) + ) + ) + mods.append(tvm.IRModule.from_expr(relay.Function([data_net1_output_1, data21], net2_output))) + 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, target): + """This function is used to generate manual pipeline configuration.""" + mod_config = {} + """The third output is the final output, the second output is for mod3, the first is for mod2 + input. + """ + pipe_config1 = { + "mod_idx": 1, + "output": [ + {"output_idx": 0, "dependent": [{"mod_idx": 2, "input_name": "data_0"}]}, + {"output_idx": 1, "dependent": [{"mod_idx": 3, "input_name": "data_0"}]}, + {"output_idx": 2, "dependent": [{"mod_idx": 0, "input_name": "0"}]}, + ], + } + mod_config[mods[0]] = { + "pipeline": pipe_config1, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": target[0], + "dev": target[1], + } + + pipe_config2 = { + "mod_idx": 2, + "output": [ + {"output_idx": 0, "dependent": [{"mod_idx": 3, "input_name": "data_1"}]}, + ], + } + mod_config[mods[1]] = { + "pipeline": pipe_config2, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": "llvm", + "dev": tvm.cpu(0), + } + + pipe_config3 = { + "mod_idx": 3, + "output": [{"output_idx": 0, "dependent": [{"mod_idx": 0, "input_name": "1"}]}], + } + mod_config[mods[2]] = { + "pipeline": pipe_config3, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": "llvm", + "dev": tvm.cpu(0), + } + return mod_config + + +def test_pipe_config_check(): + """This function is used to trigger runtime error by appling wrong logic connection.""" + + """Get three pipeline modules here. 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]
