Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,634 Bytes
d711508 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
# 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
|