mirror of https://github.com/llvm/torch-mlir
82 lines
2.6 KiB
Python
82 lines
2.6 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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import torch
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from mlir.ir import *
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from mlir.passmanager import *
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from npcomp.compiler.generic.backend import refjit as refjit_backend
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from npcomp.compiler.utils import logging
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from .abc import NpcompBackend
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__all__ = [
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"is_enabled",
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"RefjitNpcompBackend",
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]
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# Re-export.
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is_enabled = refjit_backend.is_enabled
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class TorchJitModuleInvoker(refjit_backend.JitModuleInvoker):
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"""Allows torch.Tensor inputs to be passed to module invocations."""
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def __getitem__(self, function_name: str):
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numpy_invoke = super().__getitem__(function_name)
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def invoke(*args):
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args = tuple(
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arg.numpy() if isinstance(arg, torch.Tensor) else arg for arg in args)
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return numpy_invoke(*args)
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return invoke
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class RefjitNpcompBackend(NpcompBackend):
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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 = refjit_backend.get_refjit()
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self._debug = logging.debug_enabled()
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def compile(self, imported_module: Module):
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"""Compiles an imported module, with a flat list of functions.
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The module is expected to be in "TCP + scalar code" form.
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TODO: More clearly define the backend contract. Generally this will
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extend to support globals, lists, and other stuff.
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Args:
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imported_module: The MLIR module consisting of funcs in the torch
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dialect.
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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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with imported_module.context as context:
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if self._debug:
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logging.debug("IR passed to RefJIT compiler backend:\n{}",
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imported_module)
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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 = PassManager()
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self._refjit.build_backend_compilation_pipeline(pm)
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pm.run(imported_module)
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if self._debug:
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logging.debug(
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"RefBackend input IR (this is what the RefBackend compiler sees):\n{}",
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imported_module)
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jit_module = self._refjit.JITModule.from_compiled_module(
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imported_module, refjit_backend.get_runtime_libs())
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return jit_module
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def load(self, jit_module) -> TorchJitModuleInvoker:
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"""Loads a compiled artifact into the runtime."""
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return TorchJitModuleInvoker(jit_module)
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