# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors
#
# 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.
# Lint as: python3
""" Metrics base class."""
import os
import re
import types
import uuid
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
from . import config
from .arrow_dataset import Dataset
from .arrow_reader import ArrowReader
from .arrow_writer import ArrowWriter
from .features import Features
from .info import DatasetInfo, MetricInfo
from .naming import camelcase_to_snakecase
from .utils import copyfunc, temp_seed
from .utils.download_manager import DownloadManager
from .utils.file_utils import DownloadConfig
from .utils.filelock import BaseFileLock, FileLock, Timeout
from .utils.logging import get_logger
logger = get_logger(__name__)
class FileFreeLock(BaseFileLock):
"""Thread lock until a file **cannot** be locked"""
def __init__(self, lock_file, *args, **kwargs):
self.filelock = FileLock(lock_file)
super().__init__(lock_file, *args, **kwargs)
def _acquire(self):
try:
self.filelock.acquire(timeout=0.01, poll_intervall=0.02) # Try to lock once
except Timeout:
# We couldn't acquire the lock, the file is locked!
self._lock_file_fd = self.filelock.lock_file
else:
# We were able to acquire the lock, the file is not yet locked!
self.filelock.release()
self._lock_file_fd = None
def _release(self):
self._lock_file_fd = None
class MetricInfoMixin:
"""This base class exposes some attributes of MetricInfo
at the base level of the Metric for easy access.
"""
def __init__(self, info: MetricInfo):
self._metric_info = info
@property
def info(self):
""":class:`datasets.MetricInfo` object containing all the metadata in the metric."""
return self._metric_info
@property
def name(self) -> str:
return self._metric_info.metric_name
@property
def experiment_id(self) -> Optional[str]:
return self._metric_info.experiment_id
@property
def description(self) -> str:
return self._metric_info.description
@property
def citation(self) -> str:
return self._metric_info.citation
@property
def features(self) -> Features:
return self._metric_info.features
@property
def inputs_description(self) -> str:
return self._metric_info.inputs_description
@property
def homepage(self) -> Optional[str]:
return self._metric_info.homepage
@property
def license(self) -> str:
return self._metric_info.license
@property
def codebase_urls(self) -> Optional[List[str]]:
return self._metric_info.codebase_urls
@property
def reference_urls(self) -> Optional[List[str]]:
return self._metric_info.reference_urls
@property
def streamable(self) -> bool:
return self._metric_info.streamable
@property
def format(self) -> Optional[str]:
return self._metric_info.format
[docs]class Metric(MetricInfoMixin):
"""A Metric is the base class and common API for all metrics.
Args:
config_name (``str``): This is used to define a hash specific to a metrics computation script and prevents the metric's data
to be overridden when the metric loading script is modified.
keep_in_memory (``bool``): keep all predictions and references in memory. Not possible in distributed settings.
cache_dir (``str``): Path to a directory in which temporary prediction/references data will be stored.
The data directory should be located on a shared file-system in distributed setups.
num_process (``int``): specify the total number of nodes in a distributed settings.
This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
process_id (``int``): specify the id of the current process in a distributed setup (between 0 and num_process-1)
This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
seed (Optional ``int``): If specified, this will temporarily set numpy's random seed when :func:`datasets.Metric.compute` is run.
experiment_id (``str``): A specific experiment id. This is used if several distributed evaluations share the same file system.
This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1).
max_concurrent_cache_files (``int``): Max number of concurrent metrics cache files (default 10000).
timeout (``Union[int, float]``): Timeout in second for distributed setting synchronization.
"""
def __init__(
self,
config_name: Optional[str] = None,
keep_in_memory: bool = False,
cache_dir: Optional[str] = None,
num_process: int = 1,
process_id: int = 0,
seed: Optional[int] = None,
experiment_id: Optional[str] = None,
max_concurrent_cache_files: int = 10000,
timeout: Union[int, float] = 100,
**kwargs,
):
# prepare info
self.config_name = config_name or "default"
info = self._info()
info.metric_name = camelcase_to_snakecase(self.__class__.__name__)
info.config_name = self.config_name
info.experiment_id = experiment_id or "default_experiment"
MetricInfoMixin.__init__(self, info) # For easy access on low level
# Safety checks on num_process and process_id
if not isinstance(process_id, int) or process_id < 0:
raise ValueError("'process_id' should be a number greater than 0")
if not isinstance(num_process, int) or num_process <= process_id:
raise ValueError("'num_process' should be a number greater than process_id")
if keep_in_memory and num_process != 1:
raise ValueError("Using 'keep_in_memory' is not possible in distributed setting (num_process > 1).")
self.num_process = num_process
self.process_id = process_id
self.max_concurrent_cache_files = max_concurrent_cache_files
self.keep_in_memory = keep_in_memory
self._data_dir_root = os.path.expanduser(cache_dir or config.HF_METRICS_CACHE)
self.data_dir = self._build_data_dir()
self.seed: int = seed or np.random.get_state()[1][0]
self.timeout: Union[int, float] = timeout
# Update 'compute' and 'add' docstring
# methods need to be copied otherwise it changes the docstrings of every instance
self.compute = types.MethodType(copyfunc(self.compute), self)
self.add_batch = types.MethodType(copyfunc(self.add_batch), self)
self.add = types.MethodType(copyfunc(self.add), self)
self.compute.__func__.__doc__ += self.info.inputs_description
self.add_batch.__func__.__doc__ += self.info.inputs_description
self.add.__func__.__doc__ += self.info.inputs_description
# self.arrow_schema = pa.schema(field for field in self.info.features.type)
self.buf_writer = None
self.writer = None
self.writer_batch_size = None
self.data = None
# This is the cache file we store our predictions/references in
# Keep it None for now so we can (cloud)pickle the object
self.cache_file_name = None
self.filelock = None
self.rendez_vous_lock = None
# This is all the cache files on which we have a lock when we are in a distributed setting
self.file_paths = None
self.filelocks = None
def __len__(self):
"""Return the number of examples (predictions or predictions/references pair)
currently stored in the metric's cache.
"""
return 0 if self.writer is None else len(self.writer)
def __repr__(self):
return (
f'Metric(name: "{self.name}", features: {self.features}, '
f'usage: """{self.inputs_description}""", '
f"stored examples: {len(self)})"
)
def _build_data_dir(self):
"""Path of this metric in cache_dir:
Will be:
self._data_dir_root/self.name/self.config_name/self.hash (if not none)/
If any of these element is missing or if ``with_version=False`` the corresponding subfolders are dropped.
"""
builder_data_dir = self._data_dir_root
builder_data_dir = os.path.join(builder_data_dir, self.name, self.config_name)
os.makedirs(builder_data_dir, exist_ok=True)
return builder_data_dir
def _create_cache_file(self, timeout=1) -> Tuple[str, FileLock]:
"""Create a new cache file. If the default cache file is used, we generated a new hash."""
file_path = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{self.process_id}.arrow")
filelock = None
for i in range(self.max_concurrent_cache_files):
filelock = FileLock(file_path + ".lock")
try:
filelock.acquire(timeout=timeout)
except Timeout:
# If we have reached the max number of attempts or we are not allow to find a free name (distributed setup)
# We raise an error
if self.num_process != 1:
raise ValueError(
f"Error in _create_cache_file: another metric instance is already using the local cache file at {file_path}. "
f"Please specify an experiment_id (currently: {self.experiment_id}) to avoid collision "
f"between distributed metric instances."
) from None
if i == self.max_concurrent_cache_files - 1:
raise ValueError(
f"Cannot acquire lock, too many metric instance are operating concurrently on this file system."
f"You should set a larger value of max_concurrent_cache_files when creating the metric "
f"(current value is {self.max_concurrent_cache_files})."
) from None
# In other cases (allow to find new file name + not yet at max num of attempts) we can try to sample a new hashing name.
file_uuid = str(uuid.uuid4())
file_path = os.path.join(
self.data_dir, f"{self.experiment_id}-{file_uuid}-{self.num_process}-{self.process_id}.arrow"
)
else:
break
return file_path, filelock
def _get_all_cache_files(self) -> Tuple[List[str], List[FileLock]]:
"""Get a lock on all the cache files in a distributed setup.
We wait for timeout second to let all the distributed node finish their tasks (default is 100 seconds).
"""
if self.num_process == 1:
if self.cache_file_name is None:
raise ValueError(
"Metric cache file doesn't exist. Please make sure that you call `add` or `add_batch` "
"at least once before calling `compute`."
)
file_paths = [self.cache_file_name]
else:
file_paths = [
os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{process_id}.arrow")
for process_id in range(self.num_process)
]
# Let's acquire a lock on each process files to be sure they are finished writing
filelocks = []
for process_id, file_path in enumerate(file_paths):
if process_id == 0: # process 0 already has its lock file
filelocks.append(self.filelock)
else:
filelock = FileLock(file_path + ".lock")
try:
filelock.acquire(timeout=self.timeout)
except Timeout:
raise ValueError(
f"Cannot acquire lock on cached file {file_path} for process {process_id}."
) from None
else:
filelocks.append(filelock)
return file_paths, filelocks
def _check_all_processes_locks(self):
expected_lock_file_names = [
os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-{process_id}.arrow.lock")
for process_id in range(self.num_process)
]
for expected_lock_file_name in expected_lock_file_names:
nofilelock = FileFreeLock(expected_lock_file_name)
try:
nofilelock.acquire(timeout=self.timeout)
except Timeout:
raise ValueError(
f"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist."
) from None
else:
nofilelock.release()
def _check_rendez_vous(self):
expected_lock_file_name = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-0.arrow.lock")
nofilelock = FileFreeLock(expected_lock_file_name)
try:
nofilelock.acquire(timeout=self.timeout)
except Timeout:
raise ValueError(
f"Expected to find locked file {expected_lock_file_name} from process {self.process_id} but it doesn't exist."
) from None
else:
nofilelock.release()
lock_file_name = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-rdv.lock")
rendez_vous_lock = FileLock(lock_file_name)
try:
rendez_vous_lock.acquire(timeout=self.timeout)
except Timeout:
raise ValueError(f"Couldn't acquire lock on {lock_file_name} from process {self.process_id}.") from None
else:
rendez_vous_lock.release()
def _finalize(self):
"""Close all the writing process and load/gather the data
from all the nodes if main node or all_process is True.
"""
if self.writer is not None:
self.writer.finalize()
self.writer = None
# release the locks of the processes > 0 so that process 0 can lock them to read + delete the data
if self.filelock is not None and self.process_id > 0:
self.filelock.release()
if self.keep_in_memory:
# Read the predictions and references
reader = ArrowReader(path=self.data_dir, info=DatasetInfo(features=self.features))
self.data = Dataset.from_buffer(self.buf_writer.getvalue())
elif self.process_id == 0:
# Let's acquire a lock on each node files to be sure they are finished writing
file_paths, filelocks = self._get_all_cache_files()
# Read the predictions and references
try:
reader = ArrowReader(path="", info=DatasetInfo(features=self.features))
self.data = Dataset(**reader.read_files([{"filename": f} for f in file_paths]))
except FileNotFoundError:
raise ValueError(
"Error in finalize: another metric instance is already using the local cache file. "
"Please specify an experiment_id to avoid collision between distributed metric instances."
) from None
# Store file paths and locks and we will release/delete them after the computation.
self.file_paths = file_paths
self.filelocks = filelocks
[docs] def compute(self, *, predictions=None, references=None, **kwargs) -> Optional[dict]:
"""Compute the metrics.
Usage of positional arguments is not allowed to prevent mistakes.
Args:
predictions (list/array/tensor, optional): Predictions.
references (list/array/tensor, optional): References.
**kwargs (optional): Keyword arguments that will be forwarded to the metrics :meth:`_compute`
method (see details in the docstring).
Return:
dict or None
- Dictionary with the metrics if this metric is run on the main process (``process_id == 0``).
- None if the metric is not run on the main process (``process_id != 0``).
"""
if predictions is not None:
self.add_batch(predictions=predictions, references=references)
self._finalize()
self.cache_file_name = None
self.filelock = None
if self.process_id == 0:
self.data.set_format(type=self.info.format)
predictions = self.data["predictions"]
references = self.data["references"]
with temp_seed(self.seed):
output = self._compute(predictions=predictions, references=references, **kwargs)
if self.buf_writer is not None:
self.buf_writer = None
del self.data
self.data = None
else:
# Release locks and delete all the cache files. Process 0 is released last.
for filelock, file_path in reversed(list(zip(self.filelocks, self.file_paths))):
logger.info(f"Removing {file_path}")
del self.data
self.data = None
del self.writer
self.writer = None
os.remove(file_path)
filelock.release()
return output
else:
return None
[docs] def add_batch(self, *, predictions=None, references=None):
"""Add a batch of predictions and references for the metric's stack.
Args:
predictions (list/array/tensor, optional): Predictions.
references (list/array/tensor, optional): References.
"""
batch = {"predictions": predictions, "references": references}
batch = self.info.features.encode_batch(batch)
if self.writer is None:
self._init_writer()
try:
self.writer.write_batch(batch)
except pa.ArrowInvalid as e:
match = re.match(r"Column 1 named references expected length (\d+) but got length (\d+)", str(e))
if match is not None:
error_msg = (
f"Mismatch in the number of predictions ({match.group(1)}) and references ({match.group(2)})"
)
else:
# lists - summarize long lists similarly to NumPy
# arrays/tensors - let the frameworks control formatting
def summarize_if_long_list(obj):
if not type(obj) == list or len(obj) <= 6:
return f"{obj}"
def format_chunk(chunk):
return ", ".join(repr(x) for x in chunk)
return f"[{format_chunk(obj[:3])}, ..., {format_chunk(obj[-3:])}]"
error_msg = (
f"Predictions and/or references don't match the expected format.\n"
f"Expected format: {self.features},\n"
f"Input predictions: {summarize_if_long_list(predictions)},\n"
f"Input references: {summarize_if_long_list(references)}"
)
raise ValueError(error_msg) from None
[docs] def add(self, *, prediction=None, reference=None):
"""Add one prediction and reference for the metric's stack.
Args:
prediction (list/array/tensor, optional): Predictions.
reference (list/array/tensor, optional): References.
"""
example = {"predictions": prediction, "references": reference}
example = self.info.features.encode_example(example)
if self.writer is None:
self._init_writer()
try:
self.writer.write(example)
except pa.ArrowInvalid:
raise ValueError(
f"Prediction and/or reference don't match the expected format.\n"
f"Expected format: {self.features},\n"
f"Input predictions: {prediction},\n"
f"Input references: {reference}"
) from None
def _init_writer(self, timeout=1):
if self.num_process > 1:
if self.process_id == 0:
file_path = os.path.join(self.data_dir, f"{self.experiment_id}-{self.num_process}-rdv.lock")
self.rendez_vous_lock = FileLock(file_path)
try:
self.rendez_vous_lock.acquire(timeout=timeout)
except TimeoutError:
raise ValueError(
f"Error in _init_writer: another metric instance is already using the local cache file at {file_path}. "
f"Please specify an experiment_id (currently: {self.experiment_id}) to avoid collision "
f"between distributed metric instances."
) from None
if self.keep_in_memory:
self.buf_writer = pa.BufferOutputStream()
self.writer = ArrowWriter(
features=self.info.features, stream=self.buf_writer, writer_batch_size=self.writer_batch_size
)
else:
self.buf_writer = None
# Get cache file name and lock it
if self.cache_file_name is None or self.filelock is None:
cache_file_name, filelock = self._create_cache_file() # get ready
self.cache_file_name = cache_file_name
self.filelock = filelock
self.writer = ArrowWriter(
features=self.info.features, path=self.cache_file_name, writer_batch_size=self.writer_batch_size
)
# Setup rendez-vous here if
if self.num_process > 1:
if self.process_id == 0:
self._check_all_processes_locks() # wait for everyone to be ready
self.rendez_vous_lock.release() # let everyone go
else:
self._check_rendez_vous() # wait for master to be ready and to let everyone go
def _info(self) -> MetricInfo:
"""Construct the MetricInfo object. See `MetricInfo` for details.
Warning: This function is only called once and the result is cached for all
following .info() calls.
Returns:
info: (MetricInfo) The metrics information
"""
raise NotImplementedError
[docs] def download_and_prepare(
self,
download_config: Optional[DownloadConfig] = None,
dl_manager: Optional[DownloadManager] = None,
):
"""Downloads and prepares dataset for reading.
Args:
download_config (:class:`DownloadConfig`, optional): Specific download configuration parameters.
dl_manager (:class:`DownloadManager`, optional): Specific download manager to use.
"""
if dl_manager is None:
if download_config is None:
download_config = DownloadConfig()
download_config.cache_dir = os.path.join(self.data_dir, "downloads")
download_config.force_download = False
dl_manager = DownloadManager(
dataset_name=self.name, download_config=download_config, data_dir=self.data_dir
)
self._download_and_prepare(dl_manager)
def _download_and_prepare(self, dl_manager):
"""Downloads and prepares resources for the metric.
This is the internal implementation to overwrite called when user calls
`download_and_prepare`. It should download all required resources for the metric.
Args:
dl_manager (:class:`DownloadManager`): `DownloadManager` used to download and cache data.
"""
return None
def _compute(self, *, predictions=None, references=None, **kwargs) -> Dict[str, Any]:
"""This method defines the common API for all the metrics in the library"""
raise NotImplementedError
def __del__(self):
if hasattr(self, "filelock") and self.filelock is not None:
self.filelock.release()
if hasattr(self, "rendez_vous_lock") and self.rendez_vous_lock is not None:
self.rendez_vous_lock.release()
if hasattr(self, "writer"): # in case it was already deleted
del self.writer
if hasattr(self, "data"): # in case it was already deleted
del self.data