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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/04/12 21:04:36 UTC
[GitHub] [tvm] tkonolige commented on a change in pull request #7823: [TVMC] A simplified TVMC API for python scripting.
tkonolige commented on a change in pull request #7823:
URL: https://github.com/apache/tvm/pull/7823#discussion_r611952222
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File path: python/tvm/driver/tvmc/model.py
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+# 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.
+"""
+The TVMC Model class and helper functions.
Review comment:
Some high level documentation would be useful here. Also example usage would be helpful. Maybe you can copy the example from the tests?
##########
File path: python/tvm/driver/tvmc/model.py
##########
@@ -0,0 +1,331 @@
+# 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.
+"""
+The TVMC Model class and helper functions.
+"""
+import os
+import json
+import tarfile
+from typing import Optional, Union, List, Dict, Callable
+import numpy as np
+
+import tvm
+from tvm import relay
+from tvm.micro import export_model_library_format
+from tvm.contrib import utils
+from tvm.relay.backend.graph_executor_factory import GraphExecutorFactoryModule
+
+from .common import TVMCException
+
+
+class TVMCModel(object):
+ """Initialize a TVMC model from a relay definition or a saved file.
+
+ Parameters
+ ----------
+ mod : tvm.IRModule, optional
+ The relay module corresponding to this model.
+ params : dict, optional
+ A parameter dictionary for the model.
+ model_path: str, optional
+ An alternative way to load a TVMCModel, the path to a previously
+ saved model.
+ name : str, optional
+ An optional name for the main library being compiled. If not specified,
+ 'default' will be used.
+ """
+
+ def __init__(
+ self,
+ mod: Optional[tvm.IRModule] = None,
+ params: Optional[Dict[str, tvm.nd.NDArray]] = None,
+ model_path: Optional[str] = None,
+ name: Optional[str] = None,
+ ):
+ if (mod is None or params is None) and (model_path is None):
+ raise TVMCException(
+ "Either mod and params must be provided "
+ "or a path to a previously saved TVMCModel"
+ )
+ self.mod = mod
+ self.params = params if params else {}
+ self.name = "default" if name is None else name
+ self.dumps = None
+ self.tuning_records = None
+ self.package_path = None
+ self._tmp_dir = utils.tempdir()
+ if model_path is not None:
+ self.load(model_path)
+
+ def save(self, model_path: str):
+ """Save the TVMCModel to disk. Note that this saves the graph representation,
+ the parameters, and the tuning records if applicable. It will not save any
+ compiled artifacts.
+
+ Parameters
+ ----------
+ model_path : str
+ A path to save this TVMCModel to.
+ """
+ temp = self._tmp_dir
+ metadata = {"model_name": self.name}
+
+ # Save metadata
+ metadata_name = "metadata.json"
+ metadata_path = temp.relpath(metadata_name)
+ with open(metadata_path, "w") as metadata_file:
+ json.dump(metadata, metadata_file, indent=2, sort_keys=True)
+
+ # Save relay graph
+ relay_name = "model.json"
+ relay_path = temp.relpath(relay_name)
+ with open(relay_path, "w") as relay_file:
+ relay_file.write(tvm.ir.save_json(self.mod))
+
+ # Save params
+ params_name = "model.params"
+ params_path = temp.relpath(params_name)
+ with open(params_path, "wb") as params_file:
+ params_file.write(relay.save_param_dict(self.params))
+
+ # Save tuning logs if they are being kept as part of this model.
+ records_name = "tuning_records"
+ records_path = self.get_default_tuning_path()
+
+ # Create a tar file.
+ with tarfile.open(model_path, "w") as tar:
+ tar.add(metadata_path, metadata_name)
+ tar.add(relay_path, relay_name)
+ tar.add(params_path, params_name)
+ if self.tuning_records == records_path:
+ tar.add(records_path, records_name)
+
+ def load(self, model_path: str):
+ """Load a TVMCModel from disk.
+
+ Parameters
+ ----------
+ model_path : str
+ A path to load the TVMCModel from.
+ """
+ temp = self._tmp_dir
+ t = tarfile.open(model_path)
+ t.extractall(temp.relpath("."))
+
+ # Load metadata.
+ metadata_path = temp.relpath("metadata.json")
+ with open(metadata_path, "r") as metadata_file:
+ metadata = json.load(metadata_file)
+ self.name = metadata["model_name"]
+
+ # Load relay IR.
+ relay_path = temp.relpath("model.json")
+ with open(relay_path, "r") as relay_file:
+ self.mod = tvm.ir.load_json(relay_file.read())
+
+ # Load parameter dictionary.
+ params_path = temp.relpath("model.params")
+ with open(params_path, "rb") as params_file:
+ self.params = relay.load_param_dict(params_file.read())
+
+ records_path = self.get_default_tuning_path()
+ if os.path.exists(records_path):
+ self.tuning_records = records_path
+
+ def get_default_tuning_path(self):
+ """Returns a default path for tuning records.
+
+ Returns
+ -------
+ tuning_records : str
+ A path in the models temporary directory that tuning
+ records can be stored.
+ """
+ return self._tmp_dir.relpath("tuning_records")
+
+ def export_package(
+ self,
+ executor_factory: GraphExecutorFactoryModule,
+ package_path: Optional[str] = None,
+ cross: Optional[Union[str, Callable]] = None,
+ ):
+ """Save this TVMCModel to file.
+
+ Parameters
+ ----------
+ executor_factory : GraphExecutorFactoryModule
+ The compiled graph factory that will be used to generate the output package.
+ package_path : str, None
+ Where the model should be saved. Note that it will be packaged as a .tar file.
+ If not provided, the package will be saved to a generically named file in tmp.
+ cross : str or callable object, optional
+ Function that performs the actual compilation.
+ """
+ if package_path is None:
+ package_path = self._tmp_dir.relpath("package.tar")
+ export_model_library_format(executor_factory, package_path, cross=cross)
+ self.package_path = package_path
+
+ def summary(self):
+ """ Print the IR corressponding to this model."""
+ print(self.mod)
+
+
+class TVMCPackage(object):
+ """Load a saved TVMCPackage from disk.
+
+ Parameters
+ ----------
+ package_path : str
+ The path to the saved TVMCPackage that will be loaded.
+ """
+
+ def __init__(self, package_path: str):
+ self._tmp_dir = utils.tempdir()
+ self.import_package(package_path)
+
+ def import_package(self, package_path: str):
+ """Load a TVMCPackage from a previously exported TVMCModel.
+
+ Parameters
+ ----------
+ package_path : str
+ The path to the saved TVMCPackage.
+ """
+ temp = self._tmp_dir
+ t = tarfile.open(package_path)
+ t.extractall(temp.relpath("."))
+
+ metadata_path = "metadata.json"
+ with open(temp.relpath(metadata_path), "r") as metadata_file:
+ metadata = json.load(metadata_file)
+ self.name = metadata["model_name"]
+ self.target = metadata["target"]
+ self.runtimes = metadata["runtimes"]
+
+ parameter_path = os.path.join("parameters", f"{self.name}.params")
+ with open(temp.relpath(parameter_path), "rb") as param_file:
+ self.params = bytearray(param_file.read())
+
+ graph_path = os.path.join("runtime-config", "graph", "graph.json")
+ with open(temp.relpath(graph_path), "r") as graph_file:
+ self.graph = graph_file.read()
+
+ ir_path = "relay.txt"
+ with open(temp.relpath(ir_path), "r") as relay_file:
+ self.mod = relay_file.read()
+
+ self.lib_path = temp.relpath(f"{self.name}.so")
+
+ def summary(self):
+ print(self.mod)
+
+
+class TVMCResult(object):
+ """Create a convenience wrapper around the output of tvmc.run
+
+ Parameters
+ ----------
+ outputs : dict
+ Outputs dictionary mapping the name of the output to its numpy value.
+ times : list of str
+ The execution times measured by the time evaluator to produce outputs.
+ """
+
+ def __init__(self, outputs: Dict[str, np.ndarray], times: List[str]):
+ self.outputs = outputs
+ self.times = times
+
+ def format_times(self):
+ """Format the mean, max, min and std of the execution times.
+
+ This has the effect of producing a small table that looks like:
+
+ Execution time summary:
Review comment:
Probably want to wrap this in a code block
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