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chore: import upstream snapshot with attribution
2026-07-13 13:36:25 +08:00

127 lines
4.0 KiB
Python

# isort: skip_file
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=redefined-builtin, wildcard-import
"""TVM: Open Deep Learning Compiler Stack."""
import multiprocessing
import sys
import os
# ffi module must load first
from tvm_ffi import register_object, register_global_func, get_global_func
# top-level alias
from .libinfo import __version__
from .base import _RUNTIME_ONLY
# tvm.runtime
from .runtime import Object
from .runtime._tensor import device, cpu, cuda, opencl, vulkan, metal
from .runtime._tensor import vpi, rocm, ext_dev, hexagon
from .runtime import DataType, DataTypeCode
# tvm.error
from . import error
# tvm.ir
from .ir import IRModule
from .ir import transform
from .ir import instrument
from . import ir
# tvm.script — must be imported before any dialect package so that
# tvm.script.register_dialect is reachable when dialect __init__.py files run.
from . import script
# tvm.tirx — registers itself via tvm.script.register_dialect in its __init__
from . import tirx
# tvm.backend — owns backend Python load hooks
from . import backend
# tvm.target
from . import target
# tvm.te
from . import te
# tvm.driver
from .driver import build, compile
# others
from . import arith
# support infra
from . import support
# Side-effect imports: register CUDA/ROCm FFI callbacks at TVM startup
from .support import rocm as _rocm, nvcc as _nvcc
# Relax contain modules that are only available in compiler package
# Do not import them if TVM is built with runtime only
if not _RUNTIME_ONLY:
# tvm.relax — registers itself via tvm.script.register_dialect in its __init__
from . import relax
# NOTE: This file should be python2 compatible so we can
# raise proper error message when user run the package using
# an older version of the python
def _should_print_backtrace():
in_pytest = "PYTEST_CURRENT_TEST" in os.environ
tvm_backtrace = os.environ.get("TVM_BACKTRACE", "0")
try:
tvm_backtrace = bool(int(tvm_backtrace))
except ValueError:
raise ValueError(f"invalid value for TVM_BACKTRACE {tvm_backtrace}, please set to 0 or 1.")
return in_pytest or tvm_backtrace
def tvm_wrap_excepthook(exception_hook):
"""Wrap given excepthook with TVM additional work."""
def wrapper(exctype, value, trbk):
"""Clean subprocesses when TVM is interrupted."""
if exctype is error.DiagnosticError and not _should_print_backtrace():
# TODO(@jroesch): consider moving to C++?
print("note: run with `TVM_BACKTRACE=1` environment variable to display a backtrace.")
else:
exception_hook(exctype, value, trbk)
if hasattr(multiprocessing, "active_children"):
# pylint: disable=not-callable
for p in multiprocessing.active_children():
p.terminate()
return wrapper
sys.excepthook = tvm_wrap_excepthook(sys.excepthook)
# Autoload loads built-in and out-of-tree backends. Out-of-tree extensions opt
# into being loaded automatically at ``import tvm`` time by declaring an entry
# point in the ``tvm.backends`` group:
# [project.entry-points."tvm.backends"] tvm_foo = "tvm_foo:_autoload".
# Autoload can be disabled via ``TVM_DEVICE_BACKEND_AUTOLOAD=0``.
from .backend._autoload_backends import _autoload_backends
_autoload_backends()