onnxruntime-tvm/python/tvm/module.py

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8.5 KiB
Python

"""Container of compiled functions of TVM."""
from __future__ import absolute_import as _abs
import struct
from collections import namedtuple
from ._ffi.function import ModuleBase, _set_class_module
from ._ffi.function import _init_api
from ._ffi.libinfo import find_include_path
from .contrib import cc as _cc, tar as _tar, util as _util
ProfileResult = namedtuple("ProfileResult", ["mean", "results"])
class Module(ModuleBase):
"""Module container of all TVM generated functions"""
def __repr__(self):
return "Module(%s, %x)" % (self.type_key, self.handle.value)
@property
def type_key(self):
"""Get type key of the module."""
return _GetTypeKey(self)
def get_source(self, fmt=""):
"""Get source code from module, if available.
Parameters
----------
fmt : str, optional
The specified format.
Returns
-------
source : str
The result source code.
"""
return _GetSource(self, fmt)
@property
def imported_modules(self):
"""Get imported modules
Returns
----------
modules : list of Module
The module
"""
nmod = _ImportsSize(self)
return [_GetImport(self, i) for i in range(nmod)]
def save(self, file_name, fmt=""):
"""Save the module to file.
This do not save the dependent device modules.
See also export_shared
Parameters
----------
file_name : str
The name of the file.
fmt : str
The format of the file.
See Also
--------
Module.export_library : export the module to shared library.
"""
_SaveToFile(self, file_name, fmt)
def export_library(self,
file_name,
fcompile=None,
**kwargs):
"""Export the module and its imported device code one library.
This function only works on host llvm modules.
It will pack all the imported modules
Parameters
----------
file_name : str
The name of the shared library.
fcompile : function(target, file_list, kwargs), optional
Compilation function to use create dynamic library.
If fcompile has attribute object_format, will compile host library
to that format. Otherwise, will use default format "o".
kwargs : dict, optional
Additional arguments passed to fcompile
"""
if self.type_key == "stackvm":
if not file_name.endswith(".stackvm"):
raise ValueError("Module[%s]: can only be saved as stackvm format."
"did you build with LLVM enabled?" % self.type_key)
self.save(file_name)
return
if not (self.type_key == "llvm" or self.type_key == "c"):
raise ValueError("Module[%s]: Only llvm and c support export shared" % self.type_key)
temp = _util.tempdir()
if fcompile is not None and hasattr(fcompile, "object_format"):
object_format = fcompile.object_format
else:
if self.type_key == "llvm":
object_format = "o"
else:
assert self.type_key == "c"
object_format = "cc"
path_obj = temp.relpath("lib." + object_format)
self.save(path_obj)
files = [path_obj]
is_system_lib = self.type_key == "llvm" and self.get_function("__tvm_is_system_module")()
if self.imported_modules:
path_cc = temp.relpath("devc.cc")
with open(path_cc, "w") as f:
f.write(_PackImportsToC(self, is_system_lib))
files.append(path_cc)
if not fcompile:
if file_name.endswith(".tar"):
fcompile = _tar.tar
else:
fcompile = _cc.create_shared
if self.type_key == "c":
kwargs.update({'options': ["-I" + path for path in find_include_path()]})
fcompile(file_name, files, **kwargs)
def time_evaluator(self, func_name, ctx, number=10, repeat=1, min_repeat_ms=0):
"""Get an evaluator that measures time cost of running function.
Parameters
----------
func_name: str
The name of the function in the module.
ctx: TVMContext
The context we should run this function on.
number: int
The number of times to run this function for taking average.
We call these runs as one `repeat` of measurement.
repeat: int, optional
The number of times to repeat the measurement.
In total, the function will be invoked (1 + number x repeat) times,
where the first one is warm up and will be discarded.
The returned result contains `repeat` costs,
each of which is an average of `number` costs.
min_repeat_ms: int, optional
The minimum duration of one `repeat` in milliseconds.
By default, one `repeat` contains `number` runs. If this parameter is set,
the parameters `number` will be dynamically adjusted to meet the
minimum duration requirement of one `repeat`.
i.e., When the run time of one `repeat` falls below this time, the `number` parameter
will be automatically increased.
Note
----
The function will be invoked (1 + number x repeat) times,
with the first call discarded in case there is lazy initialization.
Returns
-------
ftimer : Function
The function that takes same argument as func and returns a ProfileResult.
The ProfileResult reports `repeat` time costs in seconds.
"""
try:
feval = _RPCTimeEvaluator(
self, func_name, ctx.device_type, ctx.device_id, number, repeat, min_repeat_ms)
def evaluator(*args):
"""Internal wrapped evaluator."""
# Wrap feval so we can add more stats in future.
blob = feval(*args)
fmt = "@" + ("d" * repeat)
results = struct.unpack(fmt, blob)
mean = sum(results) / float(repeat)
return ProfileResult(mean=mean, results=results)
return evaluator
except NameError:
raise NameError("time_evaluate is only supported when RPC is enabled")
def system_lib():
"""Get system-wide library module singleton.
System lib is a global module that contains self register functions in startup.
Unlike normal dso modules which need to be loaded explicitly.
It is useful in environments where dynamic loading api like dlopen is banned.
To build system lib function, simply specify target option ```llvm --system-lib```
The system lib will be available as long as the result code is linked by the program.
The system lib is intended to be linked and loaded during the entire life-cyle of the program.
If you want dynamic loading features, use dso modules instead.
Returns
-------
module : Module
The system-wide library module.
"""
return _GetSystemLib()
def load(path, fmt=""):
"""Load module from file.
Parameters
----------
path : str
The path to the module file.
fmt : str, optional
The format of the file, if not specified
it will be inferred from suffix of the file.
Returns
-------
module : Module
The loaded module
Note
----
This function will automatically call
cc.create_shared if the path is in format .o or .tar
"""
# High level handling for .o and .tar file.
# We support this to be consistent with RPC module load.
if path.endswith(".o"):
_cc.create_shared(path + ".so", path)
path += ".so"
elif path.endswith(".tar"):
tar_temp = _util.tempdir()
_tar.untar(path, tar_temp.temp_dir)
files = [tar_temp.relpath(x) for x in tar_temp.listdir()]
_cc.create_shared(path + ".so", files)
path += ".so"
# Redirect to the load API
return _LoadFromFile(path, fmt)
def enabled(target):
"""Whether module runtime is enabled for target
Parameters
----------
target : str
The target device type.
Returns
-------
enabled : bool
Whether runtime is enabled.
Examples
--------
The following code checks if gpu is enabled.
>>> tvm.module.enabled("gpu")
"""
return _Enabled(target)
_init_api("tvm.module")
_set_class_module(Module)