def is_ipex_available():
def get_major_and_minor_from_version(full_version):
return str(version.parse(full_version).major) + "." + str(version.parse(full_version).minor)
if not is_torch_available() or importlib.util.find_spec("intel_extension_for_pytorch") is None:
return False
_ipex_version = "N/A"
try:
_ipex_version = importlib_metadata.version("intel_extension_for_pytorch")
except importlib_metadata.PackageNotFoundError:
return False
torch_major_and_minor = get_major_and_minor_from_version(_torch_version)
ipex_major_and_minor = get_major_and_minor_from_version(_ipex_version)
if torch_major_and_minor != ipex_major_and_minor:
logger.warning(
f"Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,"
f" but PyTorch {_torch_version} is found. Please switch to the matching version and run again."
)
return False
return True
# 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 sys
from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import (
importlib_metadata,
require_version,
require_version_core,
require_version_examples,
)
numpy_ver = importlib_metadata.version("numpy")
python_ver = ".".join([str(x) for x in sys.version_info[:3]])
class DependencyVersionCheckTest(TestCasePlus):
def test_core(self):
# lt + different version strings
require_version_core("numpy<1000.4.5")
require_version_core("numpy<1000.4")
require_version_core("numpy<1000")
# le
require_version_core("numpy<=1000.4.5")
require_version_core(f"numpy<={numpy_ver}")
# eq
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
ENV_VARS_TRUE_VALUES = {"1", "ON", "YES", "TRUE"}
ENV_VARS_TRUE_AND_AUTO_VALUES = ENV_VARS_TRUE_VALUES.union({"AUTO"})
USE_TF = os.environ.get("USE_TF", "AUTO").upper()
USE_TORCH = os.environ.get("USE_TORCH", "AUTO").upper()
USE_JAX = os.environ.get("USE_FLAX", "AUTO").upper()
_torch_version = "N/A"
if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
_torch_available = importlib.util.find_spec("torch") is not None
if _torch_available:
try:
_torch_version = importlib_metadata.version("torch")
logger.info(f"PyTorch version {_torch_version} available.")
except importlib_metadata.PackageNotFoundError:
_torch_available = False
else:
logger.info("Disabling PyTorch because USE_TF is set")
_torch_available = False
_tf_version = "N/A"
if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
_tf_available = importlib.util.find_spec("tensorflow") is not None
if _tf_available:
candidates = (
"tensorflow",
"tensorflow-cpu",
"tensorflow-gpu",
def run_hp_search_sigopt(trainer, n_trials: int, direction: str,
**kwargs) -> BestRun:
import sigopt
from transformers.utils.versions import importlib_metadata
if importlib_metadata.version("sigopt") >= "8.0.0":
sigopt.set_project("huggingface")
experiment = sigopt.create_experiment(
name="huggingface-tune",
type="offline",
parameters=trainer.hp_space(None),
metrics=[
dict(name="objective",
objective=direction,
strategy="optimize")
],
parallel_bandwidth=1,
budget=n_trials,
)
logger.info(
f"created experiment: https://app.sigopt.com/experiment/{experiment.id}"
)
for run in experiment.loop():
with run:
trainer.objective = None
trainer.train(resume_from_checkpoint=None, trial=run.run)
# If there hasn't been any evaluation during the training loop.
if getattr(trainer, "objective", None) is None:
metrics = trainer.evaluate()
trainer.objective = trainer.compute_objective(metrics)
run.log_metric("objective", trainer.objective)
best = list(experiment.get_best_runs())[0]
best_run = BestRun(best.id, best.values["objective"].value,
best.assignments)
else:
from sigopt import Connection
conn = Connection()
proxies = kwargs.pop("proxies", None)
if proxies is not None:
conn.set_proxies(proxies)
experiment = conn.experiments().create(
name="huggingface-tune",
parameters=trainer.hp_space(None),
metrics=[
dict(name="objective",
objective=direction,
strategy="optimize")
],
parallel_bandwidth=1,
observation_budget=n_trials,
project="huggingface",
)
logger.info(
f"created experiment: https://app.sigopt.com/experiment/{experiment.id}"
)
while experiment.progress.observation_count < experiment.observation_budget:
suggestion = conn.experiments(experiment.id).suggestions().create()
trainer.objective = None
trainer.train(resume_from_checkpoint=None, trial=suggestion)
# If there hasn't been any evaluation during the training loop.
if getattr(trainer, "objective", None) is None:
metrics = trainer.evaluate()
trainer.objective = trainer.compute_objective(metrics)
values = [dict(name="objective", value=trainer.objective)]
obs = conn.experiments(experiment.id).observations().create(
suggestion=suggestion.id, values=values)
logger.info(
f"[suggestion_id, observation_id]: [{suggestion.id}, {obs.id}]"
)
experiment = conn.experiments(experiment.id).fetch()
best = list(
conn.experiments(
experiment.id).best_assignments().fetch().iterate_pages())[0]
best_run = BestRun(best.id, best.value, best.assignments)
return best_run