peft/import_utils.py

# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed 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.
import importlib
import importlib.metadata as importlib_metadata
from functools import lru_cache

import packaging.version


@lru_cache
def is_bnb_available() -> bool:
    return importlib.util.find_spec("bitsandbytes") is not None


@lru_cache
def is_bnb_4bit_available() -> bool:
    if not is_bnb_available():
        return False

    import bitsandbytes as bnb

    return hasattr(bnb.nn, "Linear4bit")


@lru_cache
def is_auto_gptq_available():
    if importlib.util.find_spec("auto_gptq") is not None:
        AUTOGPTQ_MINIMUM_VERSION = packaging.version.parse("0.5.0")
        version_autogptq = packaging.version.parse(importlib_metadata.version("auto_gptq"))
        if AUTOGPTQ_MINIMUM_VERSION <= version_autogptq:
            return True
        else:
            raise ImportError(
                f"Found an incompatible version of auto-gptq. Found version {version_autogptq}, "
                f"but only versions above {AUTOGPTQ_MINIMUM_VERSION} are supported"
            )


@lru_cache
def is_optimum_available() -> bool:
    return importlib.util.find_spec("optimum") is not None


@lru_cache
def is_torch_tpu_available(check_device=True):
    "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
    if importlib.util.find_spec("torch_xla") is not None:
        if check_device:
            # We need to check if `xla_device` can be found, will raise a RuntimeError if not
            try:
                import torch_xla.core.xla_model as xm

                _ = xm.xla_device()
                return True
            except RuntimeError:
                return False
        return True
    return False


@lru_cache
def is_aqlm_available():
    return importlib.util.find_spec("aqlm") is not None


@lru_cache
def is_auto_awq_available():
    return importlib.util.find_spec("awq") is not None


@lru_cache
def is_eetq_available():
    return importlib.util.find_spec("eetq") is not None


@lru_cache
def is_hqq_available():
    return importlib.util.find_spec("hqq") is not None