huajsj commented on a change in pull request #8702:
URL: https://github.com/apache/tvm/pull/8702#discussion_r704902170



##########
File path: python/tvm/contrib/pipeline_executor.py
##########
@@ -0,0 +1,533 @@
+# 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 get 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 IRModule with configurations of pipe_configs then
+    return Module list and Module dependency configuration.
+
+    Parameters
+    ----------
+    pipe_configs: PipelineConfig
+        build configuration informaton.
+
+    Returns
+    -------
+    ret: PipelineExecutorFactoryModule
+        the class that wrap module list and module dependency 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_idx = mconf["mod_idx"] - 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_idx] = mconf
+        # associate mod with device
+        mods[mod] = {"dev": dev}
+
+    return PipelineExecutorFactoryModule(mods, string_config)
+
+
+def create(pipe_executor_factory_module):
+    """Create a pipeline runtime executor.
+
+    Parameters
+    ----------
+    pipe_executor_factory_module : PipelineExecutorFactoryModule
+        Executor factory with IRModule list and pipeline configuration.
+
+    Returns
+    -------
+    submodule : PipelineModule
+        Runtime pipeline module.
+    """
+
+    return PipelineModule(pipe_executor_factory_module)
+
+
+class PipelineModule(object):
+    """Wrapper of runtime module.
+
+    Parameters
+    ----------
+    pipeline_config : Dict[GraphExecutorFactoryModule, 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"
+        pipeline_create = tvm._ffi.get_global_func(
+            "tvm.pipeline_executor.create", allow_missing=False
+        )
+        assert pipeline_create
+        module = pipeline_create(mods, config)
+
+        self.module_ = module
+
+    def graph_executor_create(self, pipeline_mods, mod_config):
+        """Create graph_executor list and return text format configuration.
+
+        Parameters
+        ----------
+        pipeline_mods : List[GraphExecutorFactoryModule]
+          list of GraphExecutorFactoryModule
+
+        mod_config : Dict[str, Any]
+            modules dependency configuration informaiton.
+
+        Returns
+        -------
+        mods : List[Module]
+            Module list.
+
+        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 Binding:
+        """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

Review comment:
       fixed.




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