[GitHub] [incubator-tvm] FrozenGene commented on a change in pull request #5753: [Draft] Support Module based interface runtime

[GitBox <gi...@apache.org>]

FrozenGene commented on a change in pull request #5753:
URL: https://github.com/apache/incubator-tvm/pull/5753#discussion_r449345174



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File path: python/tvm/runtime/graph_runtime_factory.py
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@@ -0,0 +1,144 @@
+# 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.
+"""Graph runtime factory."""
+import numpy as np
+import warnings
+from tvm._ffi.base import string_types
+from tvm._ffi.registry import get_global_func
+from tvm._ffi.runtime_ctypes import TVMContext
+from tvm.contrib.graph_runtime import get_device_ctx
+from .packed_func import _set_class_module
+from tvm.rpc import base as rpc_base
+from .module import Module
+from . import ndarray
+
+
+def create(graph_runtime_kind, graph_json_str, libmod, params, module_name='default'):
+    """Create a runtime executor module given a graph and module.
+    Parameters
+    ----------
+    graph_runtime_kind: str
+        The kind of graph runtime. Like graphruntime, vm and so on.
+    graph_json_str : str or graph class
+        The graph to be deployed in json format output by nnvm graph.
+        The graph can only contain one operator(tvm_op) that
+        points to the name of PackedFunc in the libmod.
+    libmod : tvm.Module
+        The module of the corresponding function
+    Returns
+    -------
+    graph_module : GraphModule
+        Runtime graph module that can be used to execute the graph.
+    """
+    if not isinstance(graph_json_str, string_types):
+        try:
+            graph_json_str = graph_json_str._tvm_graph_json()
+        except AttributeError:
+            raise ValueError("Type %s is not supported" % type(graph_json_str))
+    fcreate = get_global_func("tvm.graph_runtime_factory.create")
+    args = []
+    for k, v in params.items():
+        args.append(k)
+        args.append(ndarray.array(v))
+    return GraphRuntimeFactoryModule(fcreate(graph_runtime_kind, graph_json_str, libmod, module_name, *args))
+
+
+class GraphRuntimeFactoryModule(Module):
+    """Graph runtime factory module.
+
+    This is a module of graph runtime factory
+
+    Parameters
+    ----------
+    module : Module
+        The interal tvm module that holds the actual graph functions.
+
+    Attributes
+    ----------
+    module : Module
+        The interal tvm module that holds the actual graph functions.
+    """
+
+    def __init__(self, module):
+        self.module = module
+        self._select_module = module["select_module"]
+        self._import_module = module["import_module"]
+        self.selected_module = None
+        self.graph_json = None
+        self.lib = None
+        self.params = {}
+        self.iter_cnt = 0
+        super(GraphRuntimeFactoryModule, self).__init__(self.module.handle)
+
+    def __del__(self):
+        pass
+
+    def runtime_create(self, ctx):
+        """Create the runtime using ctx
+
+        Parameters
+        ----------
+        ctx : TVMContext or list of TVMContext
+        """
+        ctx, num_rpc_ctx, device_type_id = get_device_ctx(self.selected_module, ctx)
+        if num_rpc_ctx == len(ctx):
+            fcreate = ctx[0]._rpc_sess.get_function("tvm.graph_runtime_factory.runtime_create")
+        else:
+            fcreate = get_global_func("tvm.graph_runtime_factory.runtime_create")
+        return fcreate(self.selected_module, *device_type_id)
+
+    def import_module(self, mod):
+        """Create the runtime using ctx
+
+        Parameters
+        ----------
+        mod : GraphRuntimeFactoryModule
+            The graph runtime factory module we want to import
+        """
+        return self._import_module(mod)
+
+    def __getitem__(self, key='default'):
+        """Get specific module
+
+        Parameters
+        ----------
+        key : str
+            The key of module.
+        """
+        self.selected_module = self._select_module(key)
+        self.selected_module._entry = self.runtime_create
+        return self.selected_module
+
+    def __iter__(self):
+        warnings.warn(
+            "legacy graph runtime behaviour of producing json / lib / params will be removed in the next release ",
+            DeprecationWarning, 2)
+        self.graph_json = self.module["get_json"]()
+        self.lib = self.module["get_lib"]()
+        for k, v in self.module["get_params"]().items():
+            self.params[k] = v
+        return self
+
+
+    def __next__(self):

Review comment:
       Please see the example of [test_legacy_compatibility](https://github.com/apache/incubator-tvm/blob/784a6bdbecc0ff3040d003c90ede5794b8be816a/tests/python/unittest/test_module_runtime_interface.py#L42). In the new design, we like to only export `compiled graph module`. However, we also want to maintain legacy compatibility in one release of TVM (and remove it in the next release). So, `__iter__` / `__next__` help us do this thing.




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