tvm-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From GitBox <...@apache.org>
Subject [GitHub] [tvm] comaniac commented on a change in pull request #8702: [Runtime] Pipeline Executor Initial patch.
Date Sat, 28 Aug 2021 22:47:36 GMT

comaniac commented on a change in pull request #8702:
URL: https://github.com/apache/tvm/pull/8702#discussion_r697926981



##########
File path: python/tvm/contrib/pipeline_executor.py
##########
@@ -0,0 +1,352 @@
+# 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 pipeline containing TVM PackedFunc."""
+import json
+import tvm._ffi
+from tvm import relay
+from tvm.contrib import graph_executor
+
+
+def pipeline_executor_enabled():
+    """check if pipeline executor enabled.
+    Return
+    ------
+    enable: bool
+        return pipeline executor get enabled or not
+    """
+    pipeline_enabled = False
+    try:
+        pipelinecreate = tvm._ffi.get_global_func("tvm.pipeline_executor.create")
+        assert pipelinecreate
+        pipeline_enabled = True
+    except ValueError:
+        print("pipeline executor not enabled!")
+
+    return pipeline_enabled
+
+
+def build_pipeline(mod_n_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 = {}
+    config_len = len(mod_n_configs)
+    string_config = [{} for _ in range(config_len)]
+    for _, (ir_mod, mod_config) in enumerate(mod_n_configs.items()):
+        # init lib_name and json_name params with empty
+        lib_name = ""
+        json_name = ""
+        params_name = ""
+        # Get module configuration
+        assert "pipeline" in mod_config and "mod_indx" in mod_config["pipeline"]
+        # Get module index in pipeline configuration
+        mconf = mod_config["pipeline"].copy()
+        # Get mod device config
+        dev = mod_config["dev"]
+        mod_indx = mconf["mod_indx"] - 1
+        target = mod_config["target"]
+        assert mod_indx < config_len
+        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["lib_name"] = lib_name
+        mconf["json_name"] = json_name
+        mconf["params_name"] = params_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 IRModule list and pipeline configuration
+    return mods, string_config
+
+
+def create(pipeline_mods, mod_config):
+    """Create a pipeline runtime executor.
+
+    Parameters
+    ----------
+    pipeline_mods : List[IRModule]
+        list of IRModule
+
+    mod_config : Dict[int, Dict[str, Any]]
+        modules and modules dependency configuration informaiton.
+
+    Returns
+    -------
+    submodule : PipelineModule
+        Runtime pipeline module.
+    """
+
+    submodule = PipelineModule(pipeline_mods, mod_config)
+    return submodule
+
+
+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, pipeline_mods, pipeline_config):
+        self.pipeline_mods = pipeline_mods
+        self.mod_config = pipeline_config
+        mods, config = self.graph_executor_create(pipeline_mods, pipeline_config)
+
+        pipelinecreate = tvm._ffi.get_global_func("tvm.pipeline_executor.create")
+        assert pipelinecreate
+        module = pipelinecreate(mods, config)
+
+        self.module_ = module
+
+    def graph_executor_create(self, pipeline_mods, mod_config):
+        """Create a pipeline runtime executor.
+
+        Parameters
+        ----------
+        pipeline_mods : List[IRModule]
+          list of IRModule
+
+        mod_config : Dict[int, Dict[str, Any]]
+            modules and modules dependency configuration informaiton.
+
+        Returns
+        -------
+        mods : GreaphModule
+            Runtime graph module.
+        """
+
+        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 PipelineModuleConfig:
+    """Pipeline Configuration Class, in this class there are 2 internal class,
+    first is Instance which use to represent Module, second is Interface which use
+    to represent Module input/output and Pipeline Module input/output, by setting
+    dependency relation between Interfaces this class can build the module
+    connection relation.
+
+    The class Hierarchical as following.
+         PipelineModuleConfig ---> Pipe   Instance ---> Interface(input/output)
+                              ---> Module Instance ---> Interface(input/output)

Review comment:
       I would just name it "ModuleWrapper" with module, input and output. So you don't need
two dicts for mod and pipe anymore.




-- 
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.

To unsubscribe, e-mail: commits-unsubscribe@tvm.apache.org

For queries about this service, please contact Infrastructure at:
users@infra.apache.org



Mime
View raw message