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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2020/07/02 16:40:45 UTC
[GitHub] [incubator-tvm] ANSHUMAN87 commented on a change in pull request #5753: [Draft] Support Module based interface runtime
ANSHUMAN87 commented on a change in pull request #5753:
URL: https://github.com/apache/incubator-tvm/pull/5753#discussion_r449121853
##########
File path: python/tvm/runtime/graph_runtime_factory.py
##########
@@ -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)
Review comment:
May be we add some fail safe check with some error messages ?
##########
File path: python/tvm/contrib/graph_runtime.py
##########
@@ -63,6 +63,15 @@ def create(graph_json_str, libmod, ctx):
return GraphModule(fcreate(graph_json_str, libmod, *device_type_id))
+# TODO (FrozenGene): rename
+def create4unified(libmod, ctx):
+ ctx, num_rpc_ctx, device_type_id = get_device_ctx(libmod, ctx)
+ if num_rpc_ctx == len(ctx):
+ fcreate = ctx[0]._rpc_sess.get_function("tvm.graph_runtime_factory.runtime_create")
Review comment:
May be we can use create_func_name variable instead of using string name directly ? We can use some global vars for such cases in the root imported file.
##########
File path: python/tvm/rpc/client.py
##########
@@ -160,7 +160,12 @@ def load_module(self, path):
m : Module
The remote module containing remote function.
"""
- return _ffi_api.LoadRemoteModule(self._sess, path)
+ module = _ffi_api.LoadRemoteModule(self._sess, path)
+ type_key = self.get_function("runtime.ModuleGetTypeKey")(module)
+ if type_key == "GraphRuntimeFactory":
+ from tvm.runtime.graph_runtime_factory import GraphRuntimeFactoryModule
+ return GraphRuntimeFactoryModule(module)
Review comment:
GraphRuntimeFactoryModule seems little off.
How about GraphModuleRuntime ?
##########
File path: python/tvm/runtime/graph_runtime_factory.py
##########
@@ -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:
I am sorry. I am little lost here. If it is possible , would you please share one example, how you are planning to use this iter & next feature?
##########
File path: python/tvm/runtime/graph_runtime_factory.py
##########
@@ -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
Review comment:
I am not sure how this "_entry" is being used . But this kind of logic always run into race condition bug, and it is very hard to debug at later point of time.
May be if we want to have a utility function associated to each module, we can have an independent one, rather than one associated with the instance.
##########
File path: python/tvm/runtime/graph_runtime_factory.py
##########
@@ -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)
Review comment:
May be selected_module should be checked whether initialized or not ?
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