mirror of https://github.com/llvm/torch-mlir
120 lines
3.2 KiB
Python
120 lines
3.2 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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import os
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from _npcomp import mlir
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from npcomp.compiler import logging
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__all__ = [
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"is_enabled",
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"CompilerBackend",
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]
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FRONTEND_PASSES = (
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"npcomp-cpa-type-inference",
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"numpy-public-functions-to-tensor",
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"convert-numpy-to-tcf",
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"convert-scf-to-std",
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"canonicalize",
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"tcf-shape-refinement",
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)
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_refjit = None
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def _get_refjit():
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"""Dynamically resolves the refjit backend native module."""
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global _refjit
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if _refjit is not None:
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return _refjit
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try:
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from _npcomp.backend import refjit as imported_refjit
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except ImportError:
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raise ImportError(
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"The npcomp native module was not compiled with refjit support")
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_refjit = imported_refjit
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return _refjit
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def is_enabled() -> bool:
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"""Returns whether the backend is enabled for the current build."""
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try:
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_get_refjit()
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return True
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except ImportError:
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return False
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def get_runtime_libs():
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resources_dir = os.path.join(os.path.dirname(__file__), "refjit_resources")
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return [os.path.join(resources_dir, "libNPCOMPCompilerRuntimeShlib.so")]
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class CompilerBackend:
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"""Main entry-point for the backend."""
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def __init__(self):
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super().__init__()
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self._refjit = _get_refjit()
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self._debug = logging.debug_enabled()
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def compile(self, imported_ir_module: mlir.ir.ModuleOp):
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"""Compiles an imported module.
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Args:
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imported_ir_module: The MLIR module as imported from the ImportFrontend.
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Returns:
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An opaque, backend specific module object that can be passed to load.
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The object may actually be something more specific to the backend (i.e.
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for IREE, it is a serialized VM flatbuffer) but the contract is that
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it is operated on by methods on this class.
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"""
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# Frontend.
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pm = mlir.passes.PassManager(imported_ir_module.context)
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pm.addPassPipelines(*FRONTEND_PASSES)
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pm.run(imported_ir_module)
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if self._debug:
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logging.debug("Frontend IR:{}", imported_ir_module.to_asm())
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# Backend.
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# Note that this is a separate pass manager purely to aid in debugging.
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pm = mlir.passes.PassManager(imported_ir_module.context)
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self._refjit.build_backend_compilation_pipeline(pm)
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pm.run(imported_ir_module)
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if self._debug:
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logging.debug("Backend IR:{}", imported_ir_module.to_asm())
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jit_module = self._refjit.JITModule.from_compiled_module(
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imported_ir_module, get_runtime_libs())
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return jit_module
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def load(self, jit_module):
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"""Loads a compiled artifact into the runtime.
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Since this is a JIT instead of an AOT compiler,
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"""
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return JitModuleInvoker(jit_module)
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class JitModuleInvoker:
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"""Wrapper around a native JitModule for calling functions."""
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def __init__(self, jit_module):
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super().__init__()
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self._jit_module = jit_module
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def __getitem__(self, function_name):
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def invoke(*args):
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results = self._jit_module.invoke(function_name, args)
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if len(results) == 1:
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# De-tuple.
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return results[0]
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else:
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return tuple(results)
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invoke.__isnpcomp__ = True
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return invoke
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