tqchen commented on a change in pull request #4855: [Refactor] move vm.py under runtime and adt to runtime.container.py URL: https://github.com/apache/incubator-tvm/pull/4855#discussion_r377425280
########## File path: python/tvm/runtime/vm.py ########## @@ -0,0 +1,357 @@ +# License .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. +# pylint: disable=no-else-return, unidiomatic-typecheck, undefined-variable, invalid-name, redefined-builtin +""" +The Relay Virtual Machine runtime. + +Implements a Python interface to executing the compiled VM object. +""" +import numpy as np + +import tvm +from tvm.runtime import Object, container +from tvm.runtime import _ffi_api +from tvm._ffi.runtime_ctypes import TVMByteArray +from tvm._ffi import base as _base + +def _convert(arg, cargs): + if isinstance(arg, Object): + cargs.append(arg) + elif isinstance(arg, np.ndarray): + nd_arr = tvm.nd.array(arg, ctx=tvm.cpu(0)) + cargs.append(nd_arr) + elif isinstance(arg, tvm.nd.NDArray): + cargs.append(arg) + elif isinstance(arg, (tuple, list)): + field_args = [] + for field in arg: + _convert(field, field_args) + cargs.append(container.tuple_object(field_args)) + elif isinstance(arg, (_base.numeric_types, bool)): + dtype = "int32" if isinstance(arg, (int, bool)) else "float32" + value = tvm.nd.array(np.array(arg, dtype=dtype), ctx=tvm.cpu(0)) + cargs.append(value) + else: + raise TypeError("Unsupported type: %s" % (type(arg))) + + +def convert(args): + cargs = [] + for arg in args: + _convert(arg, cargs) + + return cargs + + +class Executable(object): + """Relay VM executable""" + def __init__(self, mod): + self.mod = mod + self._function_params = {} + self._save = self.mod["save"] + self._get_lib = self.mod["get_lib"] + self._get_bytecode = self.mod["get_bytecode"] + self._get_stats = self.mod["get_stats"] + self._get_function_arity = self.mod["get_function_arity"] + self._get_function_param_name = self.mod["get_function_param_name"] + + def save(self): + """Save the Relay VM Executable. + + Returns + ------- + code : bytearray + The binary blob representing a serialized Relay VM executable. It + can then be saved to disk and later deserialized into a new + Executable. + + lib : :py:class:`~tvm.runtime.Module` + The runtime module that contains the generated code. It is + basically a library that is composed of hardware dependent code. + + Notes + ----- + The returned code is organized with the following sections in order. + - Global section. This section contains the globals used by the + virtual machine. + - Constant section. This section is used to store the constant pool of + a virtual machine. + - Primitive name section. This section is introduced to accommodate + the list of primitive operator names that will be invoked by the + virtual machine. + - Code section. The VM functions, including bytecode, are sitting in + this section. + + Examples + -------- + + .. code-block:: python + + import numpy as np + import tvm + from tvm import relay + # define a simple network. + x = relay.var('x', shape=(10, 10)) + f = relay.Function([x], x + x) + mod = relay.Module({"main": f}) + # create a Relay VM. + ctx = tvm.cpu() + target = "llvm" + executable = relay.vm.compile(mod, target) + code, lib = executable.save() + # save and load the code and lib file. + tmp = tvm.contrib.util.tempdir() + path_lib = tmp.relpath("lib.so") + lib.export_library(path_lib) + with open(tmp.relpath("code.ro"), "wb") as fo: + fo.write(code) + loaded_lib = tvm.runtime.load_module(path_lib) + loaded_code = bytearray(open(tmp.relpath("code.ro"), "rb").read()) + # deserialize. + des_exec = tvm.runtime.vm.Executable.load_exec(loaded_code, loaded_code) + # execute the deserialized executable. + x_data = np.random.rand(10, 10).astype('float32') + des_vm = tvm.runtime.vm.VirtualMachine(des_exec) + des_vm.init(ctx) + res = des_vm.run(x_data) + print(res.asnumpy()) + """ + return self._save(), self._get_lib() + + @staticmethod + def load_exec(bytecode, lib): + """Construct an executable from saved artifacts. + + Parameters + ---------- + bytecode : bytearray + The binary blob representing a the Relay VM bytecode. + + lib : :py:class:`~tvm.runtime.Module` + The runtime module that contains the generated code. + + Returns + ------- + exec: Executable + An executable constructed using the provided artifacts. + """ + if isinstance(bytecode, (bytes, str)): + code = bytearray(bytecode) + elif not isinstance(bytecode, (bytearray, TVMByteArray)): + raise TypeError("bytecode is expected to be the type of bytearray " + + "or TVMByteArray, but received {}".format(type(code))) + + if lib is not None and not isinstance(lib, tvm.runtime.Module): + raise TypeError("lib is expected to be the type of tvm.runtime.Module" + + ", but received {}".format(type(lib))) + + return Executable(_ffi_api.Load_Executable(bytecode, lib)) + + @property + def lib(self): + """Get the library that contains hardware dependent code. + + Returns + ------- + ret : :py:class:`~tvm.Module` Review comment: rename tvm.Module -> tvm.runtime.Module ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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