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Unverified Commit 502cf264 by Zhi Committed by GitHub
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[Refactor] move vm.py under runtime and adt to runtime.container.py (#4855)

parent 4fce5137
Showing with 481 additions and 440 deletions
python/tvm/container.py
......@@ -17,7 +17,7 @@
"""Container data structures used in TVM DSL."""
import tvm._ffi
from tvm.runtime import Object, ObjectTypes
from tvm.runtime import Object
from tvm.runtime.container import getitem_helper
from tvm.runtime import _ffi_node_api
from . import _api_internal
......@@ -104,56 +104,3 @@ class LoweredFunc(Object):
MixedFunc = 0
HostFunc = 1
DeviceFunc = 2
@tvm._ffi.register_object("vm.ADT")
class ADT(Object):
"""Algebatic data type(ADT) object.
Parameters
----------
tag : int
The tag of ADT.
fields : list[Object] or tuple[Object]
The source tuple.
"""
def __init__(self, tag, fields):
for f in fields:
assert isinstance(f, ObjectTypes), "Expect object or " \
"tvm NDArray type, but received : {0}".format(type(f))
self.__init_handle_by_constructor__(_ADT, tag, *fields)
@property
def tag(self):
return _GetADTTag(self)
def __getitem__(self, idx):
return getitem_helper(
self, _GetADTFields, len(self), idx)
def __len__(self):
return _GetADTSize(self)
def tuple_object(fields=None):
"""Create a ADT object from source tuple.
Parameters
----------
fields : list[Object] or tuple[Object]
The source tuple.
Returns
-------
ret : ADT
The created object.
"""
fields = fields if fields else []
for f in fields:
assert isinstance(f, ObjectTypes), "Expect object or tvm " \
"NDArray type, but received : {0}".format(type(f))
return _Tuple(*fields)
tvm._ffi._init_api("tvm.container")
python/tvm/relay/__init__.py
......@@ -37,7 +37,6 @@ from . import debug
from . import param_dict
from . import feature
from .backend import vm
from .backend import profiler_vm
# Root operators
from .op import Op
......
python/tvm/relay/backend/interpreter.py
......@@ -20,7 +20,7 @@ from __future__ import absolute_import
import numpy as np
from tvm import container
from tvm.runtime import container
from . import _backend
from .. import _make, analysis, transform
from .. import module
......
python/tvm/relay/backend/vm.py
......@@ -24,343 +24,11 @@ import numpy as np
import tvm
import tvm.runtime.ndarray as _nd
from tvm.runtime import Object
from tvm import autotvm, container
import tvm.runtime.vm as vm_rt
from tvm import autotvm
from tvm.relay import expr as _expr
from tvm._ffi.runtime_ctypes import TVMByteArray
from tvm._ffi import base as _base
from tvm.relay.backend.interpreter import Executor
from . import _vm
from .interpreter import Executor
def _convert(arg, cargs):
if isinstance(arg, _expr.Constant):
cargs.append(arg.data)
elif 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 = relay.vm.Executable.load_exec(loaded_code, loaded_code)
# execute the deserialized executable.
x_data = np.random.rand(10, 10).astype('float32')
des_vm = relay.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(_vm.Load_Executable(bytecode, lib))
@property
def lib(self):
"""Get the library that contains hardware dependent code.
Returns
-------
ret : :py:class:`~tvm.Module`
The runtime module that contains hardware dependent code.
"""
return self._get_lib()
@property
def stats(self):
"""Get the statistics of the Relay VM executable.
Returns
-------
ret : String
The statistic information of the VM executable.
"""
return self._get_stats()
@property
def primitive_ops(self):
"""Get the name of the primitive ops contained in the executable.
Returns
-------
ret : List[String]
The list of primitive ops.
"""
ret = []
num_primitives = _vm.GetNumOfPrimitives(self.module)
for i in range(num_primitives):
ret.append(_vm.GetPrimitiveFields(self.module, i))
return ret
@property
def bytecode(self):
"""Get the bytecode of the Relay VM executable.
Returns
-------
ret : String
The bytecode of the executable.
Notes
-----
The bytecode is in the following format:
func_name reg_file_size num_instructions
param1 param2 ... paramM
instruction1
instruction2
...
instructionN
Each instruction is printed in the following format:
hash opcode field1 ... fieldX # The text format.
The part starting from # is only used for visualization and debugging.
The real serialized code doesn't contain it, therefore the deserializer
doesn't need to deal with it as well.
"""
return self._get_bytecode()
@property
def globals(self):
"""Get the globals used by the Relay VM executable.
Returns
-------
ret : List[String]
The globals contained in the executable.
"""
ret = []
num_globals = _vm.GetNumOfGlobals(self.module)
for i in range(num_globals):
ret.append(_vm.GetGlobalFields(self.module, i))
return ret
@property
def module(self):
"""Return the runtime module contained in a virtual machine executable."""
return self.mod
def get_function_params(self, func_name):
"""Get VM Function parameters"""
if func_name in self._function_params:
return self._function_params[func_name]
arity = self._get_function_arity(func_name)
assert arity >= 0
params = []
for i in range(arity):
p = self._get_function_param_name(func_name, i)
assert p
params.append(p)
self._function_params[func_name] = params
return params
class VirtualMachine(object):
"""Relay VM runtime."""
def __init__(self, mod):
if not isinstance(mod, (Executable, tvm.runtime.Module)):
raise TypeError("mod is expected to be the type of Executable or " +
"tvm.Module, but received {}".format(type(mod)))
m = mod.module if isinstance(mod, Executable) else mod
self.mod = _vm._VirtualMachine(m)
self._exec = mod
self._init = self.mod["init"]
self._invoke = self.mod["invoke"]
self._set_input = self.mod["set_input"]
def init(self, ctx):
"""Initialize the context in the VM.
Parameters
----------
ctx : :py:class:`TVMContext`
The runtime context to run the code on.
"""
args = [ctx.device_type, ctx.device_id]
self._init(*args)
def set_input(self, func_name, *args, **kwargs):
"""Set the input to a function.
Parameters
----------
func_name : str
The name of the function.
args : list[NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to NDArray or np.ndarray
Named arguments to the function.
"""
if kwargs:
func_params = self._exec.get_function_params(func_name)
new_args = [None] * len(func_params)
assert len(args) + len(kwargs) == len(func_params)
for k in kwargs:
idx = func_params.index(k)
new_args[idx] = kwargs[k]
idx = 0
for i, arg in enumerate(new_args):
if arg is None:
new_args[i] = args[idx]
idx += 1
args = new_args
cargs = convert(args)
self._set_input(func_name, *cargs)
def invoke(self, func_name, *args, **kwargs):
"""Invoke a function.
Parameters
----------
func_name : str
The name of the function.
args : list[NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to NDArray or np.ndarray
Named arguments to the function.
Returns
-------
result : Object
The output.
"""
if args or kwargs:
self.set_input(func_name, *args, **kwargs)
return self._invoke(func_name)
def run(self, *args, **kwargs):
"""Run the main function.
Parameters
----------
args : list[NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to NDArray or np.ndarray
Named arguments to the function.
Returns
-------
result : Object
The output.
"""
return self.invoke("main", *args, **kwargs)
def compile(mod, target=None, target_host=None, params=None):
......@@ -391,7 +59,7 @@ def compile(mod, target=None, target_host=None, params=None):
Returns
-------
exec : Executable
exec : tvm.runtime.vm.Executable
The VM executable that contains both library code and bytecode.
"""
compiler = VMCompiler()
......@@ -501,10 +169,10 @@ class VMCompiler(object):
Returns
-------
exec : Executable
exec : tvm.runtime.vm.Executable
The VM executable that contains both library code and bytecode.
"""
return Executable(self._get_exec())
return vm_rt.Executable(self._get_exec())
def _update_target(self, target):
"""Update target."""
......@@ -549,6 +217,7 @@ class VMCompiler(object):
tophub_context = autotvm.util.EmptyContext()
return tophub_context
class VMExecutor(Executor):
"""
An implementation of the executor interface for
......@@ -556,7 +225,7 @@ class VMExecutor(Executor):
Useful interface for experimentation and debugging
the VM can also be used directly from the API.
supported by `tvm.relay.vm`.
supported by `tvm.runtime.vm`.
Parameters
----------
......@@ -576,7 +245,7 @@ class VMExecutor(Executor):
self.ctx = ctx
self.target = target
self.executable = compile(mod, target)
self.vm = VirtualMachine(self.executable)
self.vm = vm_rt.VirtualMachine(self.executable)
self.vm.init(ctx)
def _make_executor(self, expr=None):
......
python/tvm/relay/testing/py_converter.py
......@@ -32,15 +32,15 @@ OUTPUT_VAR_NAME = '_py_out'
# import numpy
# import tvm
# from tvm import relay
# from tvm import import container as _container
# from tvm import nd
# from tvm.runtime import import container as _container
# from tvm.relay.backend.interpreter import RefValue, ConstructorValue
PROLOGUE = [
ast.Import([alias('numpy', None)]),
ast.Import([alias('tvm', None)]),
ast.ImportFrom('tvm', [alias('relay', None)], 0),
ast.ImportFrom('tvm', [alias('nd', None)], 0),
ast.ImportFrom('tvm', [alias('container', '_container')],
ast.ImportFrom('tvm.runtime', [alias('container', '_container')],
0),
ast.ImportFrom('tvm.relay.backend.interpreter',
[alias('RefValue', None),
......
python/tvm/runtime/container.py
......@@ -15,6 +15,9 @@
# specific language governing permissions and limitations
# under the License.
"""Runtime container structures."""
import tvm._ffi
from tvm.runtime import Object, ObjectTypes
def getitem_helper(obj, elem_getter, length, idx):
"""Helper function to implement a pythonic getitem function.
......@@ -54,3 +57,56 @@ def getitem_helper(obj, elem_getter, length, idx):
if idx < 0:
idx += length
return elem_getter(obj, idx)
@tvm._ffi.register_object("vm.ADT")
class ADT(Object):
"""Algebatic data type(ADT) object.
Parameters
----------
tag : int
The tag of ADT.
fields : list[Object] or tuple[Object]
The source tuple.
"""
def __init__(self, tag, fields):
for f in fields:
assert isinstance(f, ObjectTypes), "Expect object or " \
"tvm NDArray type, but received : {0}".format(type(f))
self.__init_handle_by_constructor__(_ADT, tag, *fields)
@property
def tag(self):
return _GetADTTag(self)
def __getitem__(self, idx):
return getitem_helper(
self, _GetADTFields, len(self), idx)
def __len__(self):
return _GetADTSize(self)
def tuple_object(fields=None):
"""Create a ADT object from source tuple.
Parameters
----------
fields : list[Object] or tuple[Object]
The source tuple.
Returns
-------
ret : ADT
The created object.
"""
fields = fields if fields else []
for f in fields:
assert isinstance(f, ObjectTypes), "Expect object or tvm " \
"NDArray type, but received : {0}".format(type(f))
return _Tuple(*fields)
tvm._ffi._init_api("tvm.runtime.container")
python/tvm/relay/backend/profiler_vm.py → python/tvm/runtime/profiler_vm.py
......@@ -20,18 +20,19 @@ The Relay Virtual Machine profiler.
Provides extra APIs for profiling vm execution.
"""
from . import vm, _vm
from tvm.runtime import _ffi_api
from . import vm
def enabled():
"""Whether vm profiler is enabled."""
return hasattr(_vm, "_VirtualMachineDebug")
return hasattr(_ffi_api, "_VirtualMachineDebug")
class VirtualMachineProfiler(vm.VirtualMachine):
"""Relay profile VM runtime."""
def __init__(self, mod):
super(VirtualMachineProfiler, self).__init__(mod)
m = mod.module if isinstance(mod, vm.Executable) else mod
self.mod = _vm._VirtualMachineDebug(m)
self.mod = _ffi_api._VirtualMachineDebug(m)
self._init = self.mod["init"]
self._invoke = self.mod["invoke"]
self._get_stat = self.mod["get_stat"]
......
python/tvm/runtime/vm.py 0 → 100644
# 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._ffi.runtime_ctypes import TVMByteArray
from tvm._ffi import base as _base
from .object import Object
from . import _ffi_api, container
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.runtime.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.runtime.Module`
The runtime module that contains hardware dependent code.
"""
return self._get_lib()
@property
def stats(self):
"""Get the statistics of the Relay VM executable.
Returns
-------
ret : String
The statistic information of the VM executable.
"""
return self._get_stats()
@property
def primitive_ops(self):
"""Get the name of the primitive ops contained in the executable.
Returns
-------
ret : List[String]
The list of primitive ops.
"""
ret = []
num_primitives = _ffi_api.GetNumOfPrimitives(self.module)
for i in range(num_primitives):
ret.append(_ffi_api.GetPrimitiveFields(self.module, i))
return ret
@property
def bytecode(self):
"""Get the bytecode of the Relay VM executable.
Returns
-------
ret : String
The bytecode of the executable.
Notes
-----
The bytecode is in the following format:
func_name reg_file_size num_instructions
param1 param2 ... paramM
instruction1
instruction2
...
instructionN
Each instruction is printed in the following format:
hash opcode field1 ... fieldX # The text format.
The part starting from # is only used for visualization and debugging.
The real serialized code doesn't contain it, therefore the deserializer
doesn't need to deal with it as well.
"""
return self._get_bytecode()
@property
def globals(self):
"""Get the globals used by the Relay VM executable.
Returns
-------
ret : List[String]
The globals contained in the executable.
"""
ret = []
num_globals = _ffi_api.GetNumOfGlobals(self.module)
for i in range(num_globals):
ret.append(_ffi_api.GetGlobalFields(self.module, i))
return ret
@property
def module(self):
"""Return the runtime module contained in a virtual machine executable."""
return self.mod
def get_function_params(self, func_name):
"""Get VM Function parameters"""
if func_name in self._function_params:
return self._function_params[func_name]
arity = self._get_function_arity(func_name)
assert arity >= 0
params = []
for i in range(arity):
p = self._get_function_param_name(func_name, i)
assert p
params.append(p)
self._function_params[func_name] = params
return params
class VirtualMachine(object):
"""Relay VM runtime."""
def __init__(self, mod):
if not isinstance(mod, (Executable, tvm.runtime.Module)):
raise TypeError("mod is expected to be the type of Executable or " +
"tvm.runtime.Module, but received {}".format(type(mod)))
m = mod.module if isinstance(mod, Executable) else mod
self.mod = _ffi_api._VirtualMachine(m)
self._exec = mod
self._init = self.mod["init"]
self._invoke = self.mod["invoke"]
self._set_input = self.mod["set_input"]
def init(self, ctx):
"""Initialize the context in the VM.
Parameters
----------
ctx : :py:class:`TVMContext`
The runtime context to run the code on.
"""
args = [ctx.device_type, ctx.device_id]
self._init(*args)
def set_input(self, func_name, *args, **kwargs):
"""Set the input to a function.
Parameters
----------
func_name : str
The name of the function.
args : list[tvm.runtime.NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to tvm.runtime.NDArray or np.ndarray
Named arguments to the function.
"""
if kwargs:
func_params = self._exec.get_function_params(func_name)
new_args = [None] * len(func_params)
assert len(args) + len(kwargs) == len(func_params)
for k in kwargs:
idx = func_params.index(k)
new_args[idx] = kwargs[k]
idx = 0
for i, arg in enumerate(new_args):
if arg is None:
new_args[i] = args[idx]
idx += 1
args = new_args
cargs = convert(args)
self._set_input(func_name, *cargs)
def invoke(self, func_name, *args, **kwargs):
"""Invoke a function.
Parameters
----------
func_name : str
The name of the function.
args : list[tvm.runtime.NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to tvm.runtime.NDArray or np.ndarray
Named arguments to the function.
Returns
-------
result : Object
The output.
"""
if args or kwargs:
self.set_input(func_name, *args, **kwargs)
return self._invoke(func_name)
def run(self, *args, **kwargs):
"""Run the main function.
Parameters
----------
args : list[tvm.runtime.NDArray] or list[np.ndarray]
The arguments to the function.
kwargs: dict of str to tvm.runtime.NDArray or np.ndarray
Named arguments to the function.
Returns
-------
result : Object
The output.
"""
return self.invoke("main", *args, **kwargs)
src/runtime/container.cc
......@@ -32,14 +32,14 @@ namespace runtime {
using namespace vm;
TVM_REGISTER_GLOBAL("container._GetADTTag")
TVM_REGISTER_GLOBAL("runtime.container._GetADTTag")
.set_body([](TVMArgs args, TVMRetValue* rv) {
ObjectRef obj = args[0];
const auto& adt = Downcast<ADT>(obj);
*rv = static_cast<int64_t>(adt.tag());
});
TVM_REGISTER_GLOBAL("container._GetADTSize")
TVM_REGISTER_GLOBAL("runtime.container._GetADTSize")
.set_body([](TVMArgs args, TVMRetValue* rv) {
ObjectRef obj = args[0];
const auto& adt = Downcast<ADT>(obj);
......@@ -47,7 +47,7 @@ TVM_REGISTER_GLOBAL("container._GetADTSize")
});
TVM_REGISTER_GLOBAL("container._GetADTFields")
TVM_REGISTER_GLOBAL("runtime.container._GetADTFields")
.set_body([](TVMArgs args, TVMRetValue* rv) {
ObjectRef obj = args[0];
int idx = args[1];
......@@ -56,7 +56,7 @@ TVM_REGISTER_GLOBAL("container._GetADTFields")
*rv = adt[idx];
});
TVM_REGISTER_GLOBAL("container._Tuple")
TVM_REGISTER_GLOBAL("runtime.container._Tuple")
.set_body([](TVMArgs args, TVMRetValue* rv) {
std::vector<ObjectRef> fields;
for (auto i = 0; i < args.size(); ++i) {
......@@ -65,7 +65,7 @@ TVM_REGISTER_GLOBAL("container._Tuple")
*rv = ADT::Tuple(fields);
});
TVM_REGISTER_GLOBAL("container._ADT")
TVM_REGISTER_GLOBAL("runtime.container._ADT")
.set_body([](TVMArgs args, TVMRetValue* rv) {
int itag = args[0];
size_t tag = static_cast<size_t>(itag);
......
src/runtime/vm/executable.cc
......@@ -738,7 +738,7 @@ void Executable::LoadCodeSection(dmlc::Stream* strm) {
}
}
TVM_REGISTER_GLOBAL("relay._vm.GetNumOfGlobals")
TVM_REGISTER_GLOBAL("runtime.GetNumOfGlobals")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......@@ -746,7 +746,7 @@ TVM_REGISTER_GLOBAL("relay._vm.GetNumOfGlobals")
*rv = static_cast<int>(exec->global_map.size());
});
TVM_REGISTER_GLOBAL("relay._vm.GetGlobalFields")
TVM_REGISTER_GLOBAL("runtime.GetGlobalFields")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......@@ -763,7 +763,7 @@ TVM_REGISTER_GLOBAL("relay._vm.GetGlobalFields")
*rv = globals[idx].first;
});
TVM_REGISTER_GLOBAL("relay._vm.GetNumOfPrimitives")
TVM_REGISTER_GLOBAL("runtime.GetNumOfPrimitives")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......@@ -772,7 +772,7 @@ TVM_REGISTER_GLOBAL("relay._vm.GetNumOfPrimitives")
});
TVM_REGISTER_GLOBAL("relay._vm.GetPrimitiveFields")
TVM_REGISTER_GLOBAL("runtime.GetPrimitiveFields")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......@@ -789,7 +789,7 @@ TVM_REGISTER_GLOBAL("relay._vm.GetPrimitiveFields")
}
});
TVM_REGISTER_GLOBAL("relay._vm.Load_Executable")
TVM_REGISTER_GLOBAL("runtime.Load_Executable")
.set_body_typed([](
std::string code,
runtime::Module lib) {
......
src/runtime/vm/profiler/vm.cc
......@@ -133,7 +133,7 @@ runtime::Module CreateVirtualMachineDebug(const Executable* exec) {
return runtime::Module(vm);
}
TVM_REGISTER_GLOBAL("relay._vm._VirtualMachineDebug")
TVM_REGISTER_GLOBAL("runtime._VirtualMachineDebug")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......
src/runtime/vm/vm.cc
......@@ -1057,7 +1057,7 @@ runtime::Module CreateVirtualMachine(const Executable* exec) {
return runtime::Module(vm);
}
TVM_REGISTER_GLOBAL("relay._vm._VirtualMachine")
TVM_REGISTER_GLOBAL("runtime._VirtualMachine")
.set_body([](TVMArgs args, TVMRetValue* rv) {
runtime::Module mod = args[0];
const auto* exec = dynamic_cast<Executable*>(mod.operator->());
......
tests/python/frontend/tensorflow/test_forward.py
......@@ -62,7 +62,7 @@ tf_dtypes = {
def vmobj_to_list(o):
if isinstance(o, tvm.nd.NDArray):
return [o.asnumpy().tolist()]
elif isinstance(o, tvm.container.ADT):
elif isinstance(o, tvm.runtime.container.ADT):
result = []
for f in o:
result.extend(vmobj_to_list(f))
......
tests/python/relay/benchmarking/benchmark_vm.py
......@@ -19,7 +19,9 @@ import numpy as np
import tvm
from tvm.contrib import graph_runtime
from tvm import relay, container
from tvm import relay
from tvm.runtime import container
from tvm.runtime import vm as vm_rt
from tvm.relay import testing
from tvm.relay import vm
......@@ -58,7 +60,7 @@ def benchmark_execution(mod,
number=2, repeat=20):
with relay.build_config(opt_level=3):
exe = vm.compile(mod, target, params=params)
rly_vm = vm.VirtualMachine(exe)
rly_vm = vm_rt.VirtualMachine(exe)
rly_vm.init(ctx)
result = rly_vm.run(data)
......
tests/python/relay/test_adt.py
......@@ -117,7 +117,7 @@ def tree_to_dict(t):
def vmobj_to_list(o, dtype="float32"):
if isinstance(o, tvm.nd.NDArray):
return [o.asnumpy().tolist()]
elif isinstance(o, tvm.container.ADT):
elif isinstance(o, tvm.runtime.container.ADT):
if len(o) == 0:
tensor_nil = p.get_var("tensor_nil", dtype=dtype)
if tensor_nil.tag == o.tag:
......
tests/python/relay/test_backend_interpreter.py
......@@ -18,7 +18,8 @@ import numpy as np
import tvm
import tvm.testing
from tvm import nd
from tvm import relay, container
from tvm import relay
from tvm.runtime import container
from tvm.relay.backend.interpreter import RefValue, ConstructorValue
from tvm.relay.scope_builder import ScopeBuilder
from tvm.relay import testing, create_executor
......
tests/python/relay/test_external_codegen.py
......@@ -18,12 +18,12 @@
import os
import sys
import numpy as np
import pytest
import tvm
import tvm.relay.testing
import tvm.relay.transform
from tvm import relay
from tvm import runtime
from tvm.contrib import util
def check_result(mod, map_inputs, out_shape, result, tol=1e-5, target="llvm",
......@@ -52,8 +52,8 @@ def check_result(mod, map_inputs, out_shape, result, tol=1e-5, target="llvm",
exe = relay.vm.compile(mod, target=target)
code, lib = exe.save()
lib = update_lib(lib)
exe = relay.vm.Executable.load_exec(code, lib)
vm = relay.vm.VirtualMachine(exe)
exe = runtime.vm.Executable.load_exec(code, lib)
vm = runtime.vm.VirtualMachine(exe)
vm.init(ctx)
out = vm.run(**map_inputs)
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)
......
tests/python/relay/test_pass_partition_graph.py
......@@ -24,6 +24,7 @@ import tvm
import tvm.relay.testing
import tvm.relay.transform as transform
from tvm import relay
from tvm import runtime
from tvm.contrib import util
from tvm.relay.annotation import compiler_begin, compiler_end
from tvm.relay.expr_functor import ExprMutator
......@@ -182,7 +183,7 @@ def check_result(mod, map_inputs, out_shape, result, tol=1e-5, target="llvm",
lib_name = 'lib.so'
lib_path = tmp_path.relpath(lib_name)
lib.export_library(lib_path, fcompile=False, **kwargs)
lib = tvm.runtime.load_module(lib_path)
lib = runtime.load_module(lib_path)
return lib
......@@ -191,8 +192,8 @@ def check_result(mod, map_inputs, out_shape, result, tol=1e-5, target="llvm",
exe = relay.vm.compile(mod, target=target, params=params)
code, lib = exe.save()
lib = update_lib(lib)
exe = relay.vm.Executable.load_exec(code, lib)
vm = relay.vm.VirtualMachine(exe)
exe = runtime.vm.Executable.load_exec(code, lib)
vm = runtime.vm.VirtualMachine(exe)
vm.init(ctx)
out = vm.run(**map_inputs)
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)
......
tests/python/relay/test_py_converter.py
......@@ -19,7 +19,7 @@ import tvm
from tvm import relay
from tvm.relay.testing import to_python, run_as_python
from tvm.relay.prelude import Prelude
from tvm.container import ADT
from tvm.runtime.container import ADT
from tvm.relay.backend.interpreter import RefValue, ConstructorValue
# helper: uses a dummy let binding to sequence a list
......
tests/python/relay/test_vm.py
......@@ -14,16 +14,16 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import numpy as np
import pytest
import tvm
import numpy as np
from tvm import runtime
from tvm import relay
from tvm.relay.scope_builder import ScopeBuilder
from tvm.relay.testing.config import ctx_list
from tvm.relay.prelude import Prelude
from tvm.relay import testing
import pytest
def check_result(args, expected_result, mod=None):
"""
......@@ -52,14 +52,14 @@ def veval(f, *args, ctx=tvm.cpu(), target="llvm"):
assert isinstance(f, relay.Module), "expected expression or module"
mod = f
exe = relay.vm.compile(mod, target)
vm = relay.vm.VirtualMachine(exe)
vm = runtime.vm.VirtualMachine(exe)
vm.init(ctx)
return vm.invoke("main", *args)
def vmobj_to_list(o):
if isinstance(o, tvm.nd.NDArray):
return [o.asnumpy().tolist()]
elif isinstance(o, tvm.container.ADT):
elif isinstance(o, tvm.runtime.container.ADT):
result = []
for f in o:
result.extend(vmobj_to_list(f))
......@@ -573,7 +573,7 @@ def test_add_op_broadcast():
def test_vm_optimize():
mod, params = testing.resnet.get_workload(batch_size=1, num_layers=18)
comp = relay.backend.vm.VMCompiler()
comp = relay.vm.VMCompiler()
opt_mod, _ = comp.optimize(mod, "llvm", params)
if __name__ == "__main__":
......
tests/python/relay/test_vm_serialization.py
......@@ -19,9 +19,10 @@
import numpy as np
import tvm
from tvm.runtime import vm as _vm
from tvm.relay import vm as rly_vm
from tvm import relay
from tvm.relay.module import Module as rly_module
from tvm.relay import vm as _vm
from tvm.relay.scope_builder import ScopeBuilder
from tvm.relay.prelude import Prelude
from tvm.contrib import util
......@@ -31,11 +32,11 @@ def create_exec(f, target="llvm", params=None):
if isinstance(f, relay.Expr):
mod = relay.Module()
mod["main"] = f
executable = _vm.compile(mod, target=target, params=params)
executable = rly_vm.compile(mod, target=target, params=params)
return executable
else:
assert isinstance(f, relay.Module), "expected mod as relay.Module"
executable = _vm.compile(f, target=target, params=params)
executable = rly_vm.compile(f, target=target, params=params)
return executable
......
tests/python/unittest/test_container.py
......@@ -18,7 +18,7 @@
import numpy as np
import tvm
from tvm import nd, relay
from tvm import container as _container
from tvm.runtime import container as _container
def test_adt_constructor():
......
tests/python/unittest/test_runtime_vm_profiler.py
......@@ -14,11 +14,10 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import tvm
import numpy as np
import pytest
import tvm
from tvm.runtime import profiler_vm
from tvm import relay
from tvm.relay.testing import resnet
......@@ -26,10 +25,10 @@ def test_basic():
mod, params = resnet.get_workload()
target = 'llvm'
ctx = tvm.cpu()
if not relay.profiler_vm.enabled():
if not profiler_vm.enabled():
return
exe = relay.vm.compile(mod, target, params=params)
vm = relay.profiler_vm.VirtualMachineProfiler(exe)
vm = profiler_vm.VirtualMachineProfiler(exe)
vm.init(ctx)
data = np.random.rand(1, 3, 224, 224).astype('float32')
......
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