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# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def info_command_factory(_):
return EnvironmentCommand()
def download_command_factory(args):
return EnvironmentCommand(args.accelerate_config_file)
class EnvironmentCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
download_parser = parser.add_parser("env")
download_parser.set_defaults(func=info_command_factory)
download_parser.add_argument(
"--accelerate-config_file",
default=None,
help="The accelerate config file to use for the default values in the launching script.",
)
download_parser.set_defaults(func=download_command_factory)
def __init__(self, accelerate_config_file, *args) -> None:
self._accelerate_config_file = accelerate_config_file
def run(self):
safetensors_version = "not installed"
if is_safetensors_available():
import safetensors
safetensors_version = safetensors.__version__
elif importlib.util.find_spec("safetensors") is not None:
import safetensors
safetensors_version = f"{safetensors.__version__} but is ignored because of PyTorch version too old."
accelerate_version = "not installed"
accelerate_config = accelerate_config_str = "not found"
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
accelerate_version = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(default_config_file):
accelerate_config = load_config_from_file(self._accelerate_config_file).to_dict()
accelerate_config_str = (
"\n".join([f"\t- {prop}: {val}" for prop, val in accelerate_config.items()])
if isinstance(accelerate_config, dict)
else f"\t{accelerate_config}"
)
pt_version = "not installed"
pt_cuda_available = "NA"
if is_torch_available():
import torch
pt_version = torch.__version__
pt_cuda_available = torch.cuda.is_available()
tf_version = "not installed"
tf_cuda_available = "NA"
if is_tf_available():
import tensorflow as tf
tf_version = tf.__version__
try:
# deprecated in v2.1
tf_cuda_available = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
tf_cuda_available = bool(tf.config.list_physical_devices("GPU"))
flax_version = "not installed"
jax_version = "not installed"
jaxlib_version = "not installed"
jax_backend = "NA"
if is_flax_available():
import flax
import jax
import jaxlib
flax_version = flax.__version__
jax_version = jax.__version__
jaxlib_version = jaxlib.__version__
jax_backend = jax.lib.xla_bridge.get_backend().platform
info = {
"`transformers` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Huggingface_hub version": huggingface_hub.__version__,
"Safetensors version": f"{safetensors_version}",
"Accelerate version": f"{accelerate_version}",
"Accelerate config": f"{accelerate_config_str}",
"PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
"Tensorflow version (GPU?)": f"{tf_version} ({tf_cuda_available})",
"Flax version (CPU?/GPU?/TPU?)": f"{flax_version} ({jax_backend})",
"Jax version": f"{jax_version}",
"JaxLib version": f"{jaxlib_version}",
"Using GPU in script?": "<fill in>",
"Using distributed or parallel set-up in script?": "<fill in>",
}
print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n")
print(self.format_dict(info))
return info
@staticmethod
def format_dict(d):
return "\n".join([f"- {prop}: {val}" for prop, val in d.items()]) + "\n"
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