mirror of https://github.com/llvm/torch-mlir
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
|
|
# See https://llvm.org/LICENSE.txt for license information.
|
|
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
|
|
|
|
from _npcomp import mlir
|
|
from npcomp.compiler import logging
|
|
|
|
__all__ = [
|
|
"is_enabled",
|
|
"CompilerBackend",
|
|
]
|
|
|
|
FRONTEND_PASSES = (
|
|
"npcomp-cpa-type-inference",
|
|
"numpy-public-functions-to-tensor",
|
|
"convert-numpy-to-tcf",
|
|
"canonicalize",
|
|
"convert-scf-to-std",
|
|
)
|
|
|
|
_refjit = None
|
|
|
|
|
|
def _get_refjit():
|
|
"""Dynamically resolves the refjit backend native module."""
|
|
global _refjit
|
|
if _refjit is not None:
|
|
return _refjit
|
|
try:
|
|
from _npcomp.backend import refjit as imported_refjit
|
|
except ImportError:
|
|
raise ImportError(
|
|
"The npcomp native module was not compiled with refjit support")
|
|
_refjit = imported_refjit
|
|
return _refjit
|
|
|
|
|
|
def is_enabled() -> bool:
|
|
"""Returns whether the backend is enabled for the current build."""
|
|
try:
|
|
_get_refjit()
|
|
return True
|
|
except ImportError:
|
|
return False
|
|
|
|
|
|
class CompilerBackend:
|
|
"""Main entry-point for the backend."""
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
self._refjit = _get_refjit()
|
|
self._debug = logging.debug_enabled()
|
|
|
|
def compile(self, imported_ir_module: mlir.ir.ModuleOp):
|
|
"""Compiles an imported module.
|
|
|
|
Args:
|
|
imported_ir_module: The MLIR module as imported from the ImportFrontend.
|
|
Returns:
|
|
An opaque, backend specific module object that can be passed to load.
|
|
The object may actually be something more specific to the backend (i.e.
|
|
for IREE, it is a serialized VM flatbuffer) but the contract is that
|
|
it is operated on by methods on this class.
|
|
"""
|
|
pm = mlir.passes.PassManager(imported_ir_module.context)
|
|
pm.addPassPipelines(*FRONTEND_PASSES)
|
|
pm.run(imported_ir_module)
|
|
if self._debug:
|
|
logging.debug("Frontend IR:{}", imported_ir_module.to_asm())
|
|
|
|
jit_module = self._refjit.JITModule.from_mlir(imported_ir_module, [])
|
|
return jit_module
|
|
|
|
def load(self, jit_module):
|
|
"""Loads a compiled artifact into the runtime.
|
|
|
|
Since this is a JIT instead of an AOT compiler,
|
|
"""
|