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# pylint: disable=no-else-return, unidiomatic-typecheck, undefined-variable, invalid-name, redefined-builtin
"""
The Relay Virtual Machine.
Implements a Python interface to compiling and executing on the Relay VM.
"""
import numpy as np
import tvm.runtime.ndarray as _nd
import tvm.runtime.vm as vm_rt
from tvm import autotvm
from tvm.relay import expr as _expr
from tvm.relay.backend.interpreter import Executor
from tvm.target import Target
from . import _vm
[文档]def compile(mod, target=None, target_host=None, params=None):
"""Compile the module to VM executable. A helper function for VMCompiler.
Parameters
----------
mod : tvm.IRModule
The Relay module to build.
target : any multi-target like object, see Target.canon_multi_target
For homogeneous compilation, the unique build target.
For heterogeneous compilation, a dictionary or list of possible build targets.
target_host : None, or any target-like object, see Target.canon_target
Host compilation target, if target is device.
When TVM compiles device specific program such as CUDA,
we also need host(CPU) side code to interact with the driver
to setup the dimensions and parameters correctly.
target_host is used to specify the host side codegen target.
By default, llvm is used if it is enabled,
otherwise a stackvm intepreter is used.
params : dict of str to NDArray
Input parameters to the graph that do not change
during inference time. Used for constant folding.
Returns
-------
exec : tvm.runtime.vm.Executable
The VM executable that contains both library code and bytecode.
"""
compiler = VMCompiler()
if params:
compiler.set_params(params)
compiler.lower(mod, target, target_host)
compiler.codegen()
return compiler.get_exec()
[文档]class VMCompiler(object):
"""Compiler that compiles Relay module to VM executable."""
def __init__(self):
self.mod = _vm._VMCompiler()
self._lower = self.mod["lower"]
self._codegen = self.mod["codegen"]
self._get_exec = self.mod["get_executable"]
self._set_params_func = self.mod["set_params"]
self._get_params_func = self.mod["get_params"]
self._optimize = self.mod["optimize"]
[文档] def set_params(self, params):
"""Set constant parameters for the model.
Parameters
----------
params : dict of str to NDArray
Input parameters to the graph that do not change
during inference time. Used for constant folding.
"""
inputs = {}
for name, param in params.items():
if isinstance(param, np.ndarray):
param = _nd.array(param)
inputs[name] = _expr.const(param)
self._set_params_func(inputs)
[文档] def get_params(self):
"""Return the updated weights."""
params = self._get_params_func()
ret = {}
for key, value in params.items():
ret[key] = value.data
return ret
[文档] def lower(self, mod, target=None, target_host=None):
"""Lower the module to VM bytecode.
Parameters
----------
mod : tvm.IRModule
The Relay module to build.
target : any multi-target like object, see Target.canon_multi_target
For homogeneous compilation, the unique build target.
For heterogeneous compilation, a dictionary or list of possible build targets.
target_host : any target-like object, see Target.canon_target
Host compilation target, if target is device.
"""
raw_targets = Target.canon_multi_target_and_host(target, target_host)
tophub_context = self._tophub_context(raw_targets)
with tophub_context:
self._lower(mod, raw_targets)
[文档] def codegen(self):
"""Generate the kernel library."""
self._codegen()
[文档] def optimize(self, mod, target=None, target_host=None, params=None):
"""Helper method that optimizes a Relay module via VM.
Parameters
----------
mod : tvm.IRModule
target : any multi-target like object, see Target.canon_multi_target
For homogeneous compilation, the unique build target.
For heterogeneous compilation, a dictionary or list of possible build targets.
target_host : any target-like object, see Target.canon_target
Host compilation target, if target is device.
params : dict of str to NDArray
Input parameters to the graph that do not change
during inference time. Used for constant folding.
Returns
-------
mod : tvm.IRModule
The optimized relay module.
params : dict
The parameters of the final module.
"""
raw_targets = Target.canon_multi_target_and_host(target, target_host)
if params:
self.set_params(params)
return self._optimize(mod, raw_targets), self.get_params()
[文档] def get_exec(self):
"""Get the VM executable.
Returns
-------
exec : tvm.runtime.vm.Executable
The VM executable that contains both library code and bytecode.
"""
return vm_rt.Executable(self._get_exec())
[文档] def _tophub_context(self, raw_targets):
"""Get the autotvm context."""
# If current dispatch context is fallback context (the default root context),
# then load pre-tuned parameters from TopHub
if isinstance(autotvm.DispatchContext.current, autotvm.FallbackContext):
tophub_context = autotvm.tophub.context(raw_targets)
else:
tophub_context = autotvm.utils.EmptyContext()
return tophub_context
[文档]class VMExecutor(Executor):
"""
An implementation of the executor interface for
the Relay VM.
Useful interface for experimentation and debugging
the VM can also be used directly from the API.
supported by `tvm.runtime.vm`.
Parameters
----------
mod : :py:class:`~tvm.IRModule`
The module to support the execution.
device : :py:class:`~tvm.runtime.Device`
The runtime device to run the code on.
target : any multi-target like object, see Target.canon_multi_target
For homogeneous compilation, the unique build target.
For heterogeneous compilation, a dictionary or list of possible build targets.
"""
def __init__(self, mod, device, target):
if mod is None:
raise RuntimeError("Must provide module to get VM executor.")
self.mod = mod
self.device = device
self.target = target
self.executable = None
self.vm = None
def _make_executor(self, expr=None):
if expr:
self.mod["main"] = expr
self.executable = compile(self.mod, self.target)
self.vm = vm_rt.VirtualMachine(self.executable, self.device)
def _vm_wrapper(*args, **kwargs):
args = self._convert_args(self.mod["main"], args, kwargs)
return self.vm.run(*args)
return _vm_wrapper
