comaniac commented on a change in pull request #8702: URL: https://github.com/apache/tvm/pull/8702#discussion_r701467431
########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline configuration Review comment: Update docstring ########## File path: tests/python/relay/test_pipeline_executor.py ########## @@ -0,0 +1,276 @@ +# 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)) + + """ + # 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, target): + """ + # 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 + """ + + """ + # third output is final output, second output for mod3, first for mod2 + # input + """ + pipe_config1 = { + "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]] = { + "pipeline": pipe_config1, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": target[0], + "dev": target[1], + } + + pipe_config2 = { + "mod_indx": 2, + "output": [ + {"output_indx": 0, "dependent": [{"mod_indx": 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_indx": 3, + "output": [{"output_indx": 0, "dependent": [{"mod_indx": 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 pipeline(target): Review comment: You may consider inline this function directly to `test_pipeline` as it is only used by one function. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): Review comment: ```suggestion for binding in inputs.values(): ``` ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) Review comment: This shouldn't be a "config". It's weird to see something like `mod_config = build(pipe_config)`. This should derive from `ExecutorFactoryModule` like `GraphExecutorFactoryModule`, so maybe call it `PipelineExecutorFactoryModule`. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. Review comment: ```suggestion """The class that use to storage module connection information. The binding can be either "input" or "output". Parameters ---------- owner : ModuleWrapper The class that own this interface, in such class there are Module information like index, module name io_type : str The type of this binding. It can be either "input" or "output". name : str/integer Binding name, for input it is string such as "data0"; for output it is the index integer such as 0. ``` ########## File path: tests/python/relay/test_pipeline_executor.py ########## @@ -0,0 +1,276 @@ +# 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)) + + """ + # 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, target): + """ + # 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 + """ + + """ + # third output is final output, second output for mod3, first for mod2 + # input + """ + pipe_config1 = { + "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]] = { + "pipeline": pipe_config1, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": target[0], + "dev": target[1], + } + + pipe_config2 = { + "mod_indx": 2, + "output": [ + {"output_indx": 0, "dependent": [{"mod_indx": 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_indx": 3, + "output": [{"output_indx": 0, "dependent": [{"mod_indx": 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 pipeline(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")) + + # Runtime error check + pipe_config_check(mod1, mod2, mod3) Review comment: Put this to a separate test. For example, you can have ```python def test_pipeline_config(): (mod1, mod2, mod3), dshape = get_mannual_mod() # Inline "pipe_config_check" here ``` This check should be target independent so it should not be tested many times. Putting it to a separate test can also help locate the problem when test failure. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") Review comment: By getting rid of the index, you should implement this check like ``` if self.io_owner == binding.io_owner: raise RuntimeError(f"Can not set self as binding.") ``` Then you need to implement `ModuleWrapper.__eq__(self, other)` to support the `==` operator. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") + + if owner_indx != 0 and self.io_type == "input": + raise RuntimeError(f"Module only can start binding from output!") + + if owner_indx != 0 and bind_owner_indx != 0 and binding.io_type == "output": + raise RuntimeError(f"Module output can not binding with module output!") + + if owner_indx != 0 and bind_owner_indx == 0 and binding.io_type == "input": + raise RuntimeError(f"Module output can not binding with global input!") + + if owner_indx == 0 and self.io_type != "input": + raise RuntimeError(f"Global only can start binding from input!") + + if owner_indx == 0 and binding.io_type != "input": + raise RuntimeError(f"Global input only can set binding with module input.") + + self.bindings.append(binding) + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): Review comment: This can also be `if not self.is_global_interface()` ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): Review comment: The word "acircle" seems not that common. Maybe just "check_dag_acyclic" and return True if it is valid (no circle)? ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") + + if owner_indx != 0 and self.io_type == "input": Review comment: Again, all `owner_idx != 0` can just be `not self.is_global_interface()`. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 Review comment: I still feel use 0 to indicate global interface is not clear enough. I read through the use cases, you only use it for `index != 0`, so it should be fine to just make this function to be ``` def is_global_interface(self): return not isinstance(self.io_owner, PipelineConfig.ModuleWrapper) ``` btw, please change all `xx_indx` to `xx_index` or `xx_idx`. ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") + + if owner_indx != 0 and self.io_type == "input": + raise RuntimeError(f"Module only can start binding from output!") + + if owner_indx != 0 and bind_owner_indx != 0 and binding.io_type == "output": + raise RuntimeError(f"Module output can not binding with module output!") + + if owner_indx != 0 and bind_owner_indx == 0 and binding.io_type == "input": + raise RuntimeError(f"Module output can not binding with global input!") + + if owner_indx == 0 and self.io_type != "input": + raise RuntimeError(f"Global only can start binding from input!") + + if owner_indx == 0 and binding.io_type != "input": + raise RuntimeError(f"Global input only can set binding with module input.") + + self.bindings.append(binding) + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + # check if the source and target data_type same + if ( + isinstance(binding.io_owner, PipelineConfig.ModuleWrapper) + and self.data_type != binding.data_type + ): + raise RuntimeError(f"Illegal type:binding type is not same!") + + binding.parents.append(self) + # Do acircle check after add the in-degree. + if not self.dag_acircle_check( + binding.io_owner, self.io_owner.input_bindings.bindings + ): + raise RuntimeError(f"Illegal connection: cause a circle!") + + def __init__(self, mod=None, index=0): + """init class""" + self.input_params = InferType()(mod)["main"].params + self.output_values = InferType()(mod)["main"].checked_type.ret_type + self.set_index_name(index) + self.mod = mod + self.input_bindings = PipelineConfig.BindingList(self, "input") + self.output_bindings = PipelineConfig.BindingList(self, "output") + self.target_host_ = None + self.build_func_ = None + self.params_ = None + self.target_ = None + self.dev_ = None + + def __getitem__(self, key): + """get item by key""" + if isinstance(key, str): + if key == "input": + return self.input_bindings + + if key == "output": + return self.output_bindings + + raise RuntimeError(f"{key} not found!") + + def get_data_type(self, key, stype): + """get module input/output data type.""" + + if stype == "input": + for param in self.input_params: + if param.name_hint == key: + return param._checked_type_ + + if stype == "output": + if isinstance(self.output_values, tvm.ir.type.TupleType): + if int(key) < len(self.output_values.fields): + return self.output_values.fields[int(key)] + elif int(key) == 0: + return self.output_values + + return None + + def set_index_name(self, index): + """generate name by index and storage index value""" + self.index = index + self.name = "mod{}".format(str(index)) + + def is_root_mod(self): + """use by DAG topology sort, identify if this item is root item and in-degree is 0""" + for _, binding in self.input_bindings.bindings.items(): + if binding.parents: + return False + + return True + + def remove_self_from_bindings(self): + """use by DAG topology sort, by remove self from binding to reduce child in-degree""" + for _, binding in self.output_bindings.bindings.items(): + for child in binding.bindings: + if binding in child.parents: + child.parents.remove(binding) + + def set_target_host(self, host): + """set target host that use by build function""" + self.target_host_ = host + + def set_build_func(self, build_func): + """set build funciton that use by build function""" + self.build_func_ = build_func + + def set_params(self, params): + """set params that use by build function""" + self.params_ = params + + def set_target(self, target): + """set target that use by build function""" + self.target_ = target + + def set_dev(self, dev): + """set dev that use by build function""" + self.dev_ = dev + + class BindingList: + """Use to storage Binding list. + Parameters + ---------- + + owner : ModuleWrapper/PipelineConfig + who own this list, it can be ModuleWrapper or PipelineConfig + + type_name : str + The type of this binding list. It can be either "input" or "output". + """ + + def __init__(self, owner, type_name): + self.bindings = {} + self.io_owner = owner + self.binding_type = type_name + + def get_binding_data_type(self, key): + """return binding data type""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.get_data_type(key, self.binding_type) + + return None + + def __getitem__(self, key): + """return item by key""" + if key not in self.bindings: + data_type = self.get_binding_data_type(key) + if not data_type and isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + raise RuntimeError(f"Illegal name: {self.binding_type}:{key} cannot find") + + self.bindings[key] = PipelineConfig.ModuleWrapper.Binding( + self.io_owner, self.binding_type, key, data_type + ) + + return self.bindings[key] + + def __init__(self): + self.last_mod_indx = 0 + self.mod_wrapper = {} + self.input_bindings = self.BindingList(self, "input") + self.output_bindings = self.BindingList(self, "output") + + def __str__(self): + """ Get configuration in string type""" + # Sort moudles + self.dag_topology_sort() + + # get input + input_dump = "Inputs\n" + for input_name in self.input_bindings.bindings: + inf = self.input_bindings.bindings[input_name] + input_dump += inf.__repr__() + "\n" + + # get connections + output = {} + connections_dump = "\nconnections\n" + for mod in self.mod_wrapper: + for _, interface in self.mod_wrapper[mod].output_bindings.bindings.items(): + if interface.bindings: + mname, dname = interface.get_name() + iname = mname + ".output(" + dname + ")->" + for dep in interface.bindings: + dep_mname, dep_dname = dep.get_name() + if isinstance(dep.io_owner, PipelineConfig.ModuleWrapper): + iname += " " + dep_mname + "." + dep_dname + connections_dump += " |" + iname + "\n" + else: + output[dep_dname] = mname + ".output(" + dname + ")" + + # get output + output_dump = "\noutput\n" + for name in sorted(output.keys()): + output_dump += " |output(" + name + ") : " + output[name] + "\n" + + return input_dump + output_dump + connections_dump + + def __getitem__(self, key): + """return item by key""" + if isinstance(key, tvm.ir.module.IRModule): + if key not in self.mod_wrapper: + # self.last_mod_indx start from 1 and be initialize value, + # the final value for mod index would get generate by function + # dag_topology_sort Review comment: In this case can we just let index unset until topological sort? It's weird to see module index has two meanings (adding order and topological order). ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") + + if owner_indx != 0 and self.io_type == "input": + raise RuntimeError(f"Module only can start binding from output!") + + if owner_indx != 0 and bind_owner_indx != 0 and binding.io_type == "output": + raise RuntimeError(f"Module output can not binding with module output!") + + if owner_indx != 0 and bind_owner_indx == 0 and binding.io_type == "input": + raise RuntimeError(f"Module output can not binding with global input!") + + if owner_indx == 0 and self.io_type != "input": + raise RuntimeError(f"Global only can start binding from input!") + + if owner_indx == 0 and binding.io_type != "input": + raise RuntimeError(f"Global input only can set binding with module input.") + + self.bindings.append(binding) + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + # check if the source and target data_type same + if ( + isinstance(binding.io_owner, PipelineConfig.ModuleWrapper) + and self.data_type != binding.data_type + ): + raise RuntimeError(f"Illegal type:binding type is not same!") Review comment: ```suggestion raise RuntimeError(f"Illegal type (%s vs. %s): binding type is not same!" % (self.data_type, binding.data_type)) ``` ########## File path: python/tvm/contrib/pipeline_executor.py ########## @@ -0,0 +1,559 @@ +# 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 + ---------- + mod_n_configs: Dict[IRModule, Dict[str, Any]] + build configuration informaton, structure like following. + {IRModule: {"target":target, + "target_host":target_host, + "params":params, + "mod_name"mod_name, + "build":build}} + + Returns + ------- + ret: List[IRModule] + list of IRModule + string_config: Dict[int, Dict[str, any]] + pipeline 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_indx = mconf["mod_indx"] - 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_indx] = mconf + # associate mod with device + mods[mod] = {"dev": dev} + + # return PipeModuleConfig + return PipeModuleConfig(mods, string_config) + + +def create(pipe_mod_config): + """Create a pipeline runtime executor. + + Parameters + ---------- + + pipe_mod_config : PipeModuleConfig + class to storage IRModule list and pipeline configuration. + ------- + + Returns + submodule : PipelineModule + Runtime pipeline module. + """ + + return PipelineModule(pipe_mod_config) + + +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 index and + Binding information. + """ + + class Binding: + """The class that use to storage module connection information. + There are 2 types Binding Input:1 Output:2 + Parameters + ---------- + + owner : ModuleWrapper + The class that own this interface, in such class there are + Module information like index, module name + + io_type : str + The type of this binding. It can be either "input" or "output". + + name : str/integer + Binding name, for input that is string for example "data0" + for output that is integer for example 0. + """ + + def __init__(self, owner, stype, name, data_type=None): + self.io_owner = owner + self.io_type = stype + self.name = str(name) + # These item that have dependency relation with self + self.bindings = [] + # The item that self depend + self.parents = [] + + self.data_type = data_type + + def get_name(self): + """get owner name and self name""" + owner_name = "" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + owner_name = self.io_owner.name + + return owner_name, self.name + + def get_owner_indx(self): + """return index if owner is ModuleWrapper, if not return 0""" + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + return self.io_owner.index + + # if not ModuleWrapper then owner is PipelineConfig, return 0 + # to identify this is global interface + return 0 + + def __repr__(self): + """Get all binding(input data), exepect like |data_0: mod1:data_0""" + ret = " |{}: ".format(self.name) + for binding in self.bindings: + mname, dname = binding.get_name() + ret += "{0}:{1} ".format(mname, dname) + return ret + + def dag_acircle_check(self, start, inputs): + """check if the DAG that current binding stay is acircle""" + for _, binding in inputs.items(): + if start == binding.io_owner: + return False + for p in binding.parents: + if not self.dag_acircle_check(start, p.io_owner.input_bindings.bindings): + return False + + return True + + def connect(self, binding): + """ + # check if the bindendency setting correct. + # correct connection are following + # 1. global input to module input + # 2. module output to global output + # 3. module output to moudle input + """ + owner_indx = self.get_owner_indx() + bind_owner_indx = binding.get_owner_indx() + if owner_indx == bind_owner_indx: + raise RuntimeError(f"can not set self as binding.") + + if owner_indx != 0 and self.io_type == "input": + raise RuntimeError(f"Module only can start binding from output!") + + if owner_indx != 0 and bind_owner_indx != 0 and binding.io_type == "output": + raise RuntimeError(f"Module output can not binding with module output!") + + if owner_indx != 0 and bind_owner_indx == 0 and binding.io_type == "input": + raise RuntimeError(f"Module output can not binding with global input!") + + if owner_indx == 0 and self.io_type != "input": + raise RuntimeError(f"Global only can start binding from input!") + + if owner_indx == 0 and binding.io_type != "input": + raise RuntimeError(f"Global input only can set binding with module input.") + + self.bindings.append(binding) + if isinstance(self.io_owner, PipelineConfig.ModuleWrapper): + # check if the source and target data_type same + if ( + isinstance(binding.io_owner, PipelineConfig.ModuleWrapper) + and self.data_type != binding.data_type + ): + raise RuntimeError(f"Illegal type:binding type is not same!") + + binding.parents.append(self) + # Do acircle check after add the in-degree. + if not self.dag_acircle_check( + binding.io_owner, self.io_owner.input_bindings.bindings + ): + raise RuntimeError(f"Illegal connection: cause a circle!") + + def __init__(self, mod=None, index=0): + """init class""" + self.input_params = InferType()(mod)["main"].params + self.output_values = InferType()(mod)["main"].checked_type.ret_type + self.set_index_name(index) + self.mod = mod + self.input_bindings = PipelineConfig.BindingList(self, "input") + self.output_bindings = PipelineConfig.BindingList(self, "output") + self.target_host_ = None + self.build_func_ = None + self.params_ = None + self.target_ = None + self.dev_ = None + + def __getitem__(self, key): + """get item by key""" + if isinstance(key, str): + if key == "input": + return self.input_bindings + + if key == "output": + return self.output_bindings + + raise RuntimeError(f"{key} not found!") + + def get_data_type(self, key, stype): + """get module input/output data type.""" + + if stype == "input": + for param in self.input_params: + if param.name_hint == key: + return param._checked_type_ + + if stype == "output": + if isinstance(self.output_values, tvm.ir.type.TupleType): + if int(key) < len(self.output_values.fields): + return self.output_values.fields[int(key)] + elif int(key) == 0: + return self.output_values + + return None + + def set_index_name(self, index): + """generate name by index and storage index value""" + self.index = index + self.name = "mod{}".format(str(index)) + + def is_root_mod(self): + """use by DAG topology sort, identify if this item is root item and in-degree is 0""" + for _, binding in self.input_bindings.bindings.items(): + if binding.parents: + return False + + return True + + def remove_self_from_bindings(self): + """use by DAG topology sort, by remove self from binding to reduce child in-degree""" + for _, binding in self.output_bindings.bindings.items(): + for child in binding.bindings: + if binding in child.parents: + child.parents.remove(binding) + + def set_target_host(self, host): + """set target host that use by build function""" + self.target_host_ = host + + def set_build_func(self, build_func): + """set build funciton that use by build function""" + self.build_func_ = build_func + + def set_params(self, params): + """set params that use by build function""" + self.params_ = params + + def set_target(self, target): + """set target that use by build function""" + self.target_ = target + + def set_dev(self, dev): + """set dev that use by build function""" + self.dev_ = dev Review comment: As I mentioned, Python doesn't have private member, so you can just name and access them normally. For example, `mod.set_target_host(host)` -> `mod.target_host = host`. ########## File path: tests/python/relay/test_pipeline_executor.py ########## @@ -0,0 +1,276 @@ +# 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)) + + """ + # 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, target): + """ + # 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 + """ + + """ + # third output is final output, second output for mod3, first for mod2 + # input + """ + pipe_config1 = { + "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]] = { + "pipeline": pipe_config1, + "target_host": None, + "mod_name": "default", + "build": None, + "params": None, + "target": target[0], + "dev": target[1], + } + + pipe_config2 = { + "mod_indx": 2, + "output": [ + {"output_indx": 0, "dependent": [{"mod_indx": 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_indx": 3, + "output": [{"output_indx": 0, "dependent": [{"mod_indx": 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 pipeline(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")) + + # Runtime error check + pipe_config_check(mod1, mod2, mod3) + + pipe_config = pipeline_executor.PipelineConfig() + + # Create pipeline compute input/output and subgraph dependent relation. + + # Test in key mode for binding find. + # pipeline compute input "data_0" would get forward to mod1 as input "data_0" + pipe_config["input"]["data_0"].connect(pipe_config[mod1]["input"]["data_0"]) + + # pipeline compute input "data_1" would get forward to mod2 as input "data_1" + pipe_config["input"]["data_1"].connect(pipe_config[mod2]["input"]["data_1"]) + + # mod1 output(0) would get forward to mod2 as input "data_0" + pipe_config[mod1]["output"][0].connect(pipe_config[mod2]["input"]["data_0"]) + + # mod1 output(1) would get forward to mod3 as input "data_0" + pipe_config[mod1]["output"][1].connect(pipe_config[mod3]["input"]["data_0"]) + + # mod2 output(0) would get forward to mod3 as input "data_1" + pipe_config[mod2]["output"][0].connect(pipe_config[mod3]["input"]["data_1"]) + + # mod1 output(2) would get forward as final pipeline compute output(1) + pipe_config[mod1]["output"][2].connect(pipe_config["output"]["0"]) + + # mod3 output(0) would get forward as final pipeline compute output(2) + pipe_config[mod3]["output"][0].connect(pipe_config["output"]["1"]) + """ + # print configueration (print(pipe_config)), 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 + """ + + """ + # set other parameter. + """ + pipe_config[mod1].set_target(target[0]) + pipe_config[mod1].set_dev(target[1]) + + pipe_config[mod2].set_target("llvm") + pipe_config[mod2].set_dev(tvm.cpu(0)) + + pipe_config[mod3].set_target("llvm") + pipe_config[mod3].set_dev(tvm.cpu(0)) + + """ + # check if the configuration match expectation. + """ + assert pipe_config.get_config() == get_manual_conf([mod1, mod2, mod3], target) + + """ + # Test build and create pipeline module + """ + with relay.build_config(opt_level=3): + pipeline_mod_config = pipeline_executor.build(pipe_config) + + pipeline_module = pipeline_executor.create(pipeline_mod_config) + assert pipeline_module + + +def pipe_config_check(mod1, mod2, mod3): + """ + # try invalid input/output name exepect runtime error + """ + pipe_error = pipeline_executor.PipelineConfig() + with pytest.raises(RuntimeError): + pipe_error[mod1]["output"][9] + + with pytest.raises(RuntimeError): + pipe_error[mod1]["input"]["data_9"] + + """ + # try cirle connection , expect runtime error + """ + with pytest.raises(RuntimeError): + pipe_error[mod1]["output"][0].connect(pipe_error[mod2]["input"]["data_0"]) + pipe_error[mod2]["output"][0].connect(pipe_error[mod1]["input"]["data_0"]) + + """ + # try wrong module order connection check, expect runtime error. + """ + + with pytest.raises(RuntimeError): + pipe_error[mod1]["output"][0].connect(pipe_error[mod1]["input"]["data_0"]) + + with pytest.raises(RuntimeError): + pipe_error[mod1]["input"]["data_0"].connect(pipe_error[mod1]["input"]["data_0"]) + + with pytest.raises(RuntimeError): + pipe_error[mod1]["input"]["data_0"].connect(pipe_error[mod2]["input"]["data_0"]) + + with pytest.raises(RuntimeError): + pipe_error[mod1]["output"][0].connect(pipe_error["input"]["data_0"]) + + with pytest.raises(RuntimeError): + pipe_error["input"]["data_0"].connect(pipe_error[mod1]["output"][0]) + + with pytest.raises(RuntimeError): + pipe_error["output"]["0"].connect(pipe_error[mod1]["output"][0]) + + +def test_pipeline(): + if pipeline_executor.pipeline_executor_enabled(): + target_list = tvm.testing.enabled_targets() + for target in target_list: + pipeline(target) + + +if __name__ == "__main__": + test_pipeline() Review comment: ```suggestion if __name__ == "__main__": pytest.main([__file__]) ``` -- This is an automated message from the Apache Git Service. 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