Source code for transformers.integrations

# 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.
"""
Integrations with other Python libraries.
"""
import functools
import importlib.util
import numbers
import os
import sys
import tempfile
from pathlib import Path

from .file_utils import is_datasets_available
from .utils import logging


logger = logging.get_logger(__name__)


# comet_ml requires to be imported before any ML frameworks
_has_comet = importlib.util.find_spec("comet_ml") is not None and os.getenv("COMET_MODE", "").upper() != "DISABLED"
if _has_comet:
    try:
        import comet_ml  # noqa: F401

        if hasattr(comet_ml, "config") and comet_ml.config.get_config("comet.api_key"):
            _has_comet = True
        else:
            if os.getenv("COMET_MODE", "").upper() != "DISABLED":
                logger.warning("comet_ml is installed but `COMET_API_KEY` is not set.")
            _has_comet = False
    except (ImportError, ValueError):
        _has_comet = False

from .file_utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available  # noqa: E402
from .trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402


# Integration functions:
def is_wandb_available():
    # any value of WANDB_DISABLED disables wandb
    if os.getenv("WANDB_DISABLED", "").upper() in ENV_VARS_TRUE_VALUES:
        logger.warning(
            "Using the `WAND_DISABLED` environment variable is deprecated and will be removed in v5. Use the "
            "--report_to flag to control the integrations used for logging result (for instance --report_to none)."
        )
        return False
    return importlib.util.find_spec("wandb") is not None


def is_comet_available():
    return _has_comet


def is_tensorboard_available():
    return importlib.util.find_spec("tensorboard") is not None or importlib.util.find_spec("tensorboardX") is not None


def is_optuna_available():
    return importlib.util.find_spec("optuna") is not None


def is_ray_available():
    return importlib.util.find_spec("ray") is not None


def is_ray_tune_available():
    if not is_ray_available():
        return False
    return importlib.util.find_spec("ray.tune") is not None


def is_sigopt_available():
    return importlib.util.find_spec("sigopt") is not None


def is_azureml_available():
    if importlib.util.find_spec("azureml") is None:
        return False
    if importlib.util.find_spec("azureml.core") is None:
        return False
    return importlib.util.find_spec("azureml.core.run") is not None


def is_mlflow_available():
    return importlib.util.find_spec("mlflow") is not None


def is_fairscale_available():
    return importlib.util.find_spec("fairscale") is not None


def is_neptune_available():
    return importlib.util.find_spec("neptune") is not None


def is_codecarbon_available():
    return importlib.util.find_spec("codecarbon") is not None


def hp_params(trial):
    if is_optuna_available():
        import optuna

        if isinstance(trial, optuna.Trial):
            return trial.params
    if is_ray_tune_available():
        if isinstance(trial, dict):
            return trial

    if is_sigopt_available():
        if isinstance(trial, dict):
            return trial

    raise RuntimeError(f"Unknown type for trial {trial.__class__}")


def default_hp_search_backend():
    if is_optuna_available():
        return "optuna"
    elif is_ray_tune_available():
        return "ray"
    elif is_sigopt_available():
        return "sigopt"


def run_hp_search_optuna(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
    import optuna

    def _objective(trial, checkpoint_dir=None):
        checkpoint = None
        if checkpoint_dir:
            for subdir in os.listdir(checkpoint_dir):
                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
                    checkpoint = os.path.join(checkpoint_dir, subdir)
        trainer.objective = None
        trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
        # 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)
        return trainer.objective

    timeout = kwargs.pop("timeout", None)
    n_jobs = kwargs.pop("n_jobs", 1)
    study = optuna.create_study(direction=direction, **kwargs)
    study.optimize(_objective, n_trials=n_trials, timeout=timeout, n_jobs=n_jobs)
    best_trial = study.best_trial
    return BestRun(str(best_trial.number), best_trial.value, best_trial.params)


def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
    import ray

    def _objective(trial, local_trainer, checkpoint_dir=None):
        try:
            from transformers.utils.notebook import NotebookProgressCallback

            if local_trainer.pop_callback(NotebookProgressCallback):
                local_trainer.add_callback(ProgressCallback)
        except ModuleNotFoundError:
            pass

        checkpoint = None
        if checkpoint_dir:
            for subdir in os.listdir(checkpoint_dir):
                if subdir.startswith(PREFIX_CHECKPOINT_DIR):
                    checkpoint = os.path.join(checkpoint_dir, subdir)
        local_trainer.objective = None
        local_trainer.train(resume_from_checkpoint=checkpoint, trial=trial)
        # If there hasn't been any evaluation during the training loop.
        if getattr(local_trainer, "objective", None) is None:
            metrics = local_trainer.evaluate()
            local_trainer.objective = local_trainer.compute_objective(metrics)
            local_trainer._tune_save_checkpoint()
            ray.tune.report(objective=local_trainer.objective, **metrics, done=True)

    if not trainer._memory_tracker.skip_memory_metrics:
        from .trainer_utils import TrainerMemoryTracker

        logger.warning(
            "Memory tracking for your Trainer is currently "
            "enabled. Automatically disabling the memory tracker "
            "since the memory tracker is not serializable."
        )
        trainer._memory_tracker = TrainerMemoryTracker(skip_memory_metrics=True)

    # The model and TensorBoard writer do not pickle so we have to remove them (if they exists)
    # while doing the ray hp search.
    _tb_writer = trainer.pop_callback(TensorBoardCallback)
    trainer.model = None

    # Setup default `resources_per_trial`.
    if "resources_per_trial" not in kwargs:
        # Default to 1 CPU and 1 GPU (if applicable) per trial.
        kwargs["resources_per_trial"] = {"cpu": 1}
        if trainer.args.n_gpu > 0:
            kwargs["resources_per_trial"]["gpu"] = 1
        resource_msg = "1 CPU" + (" and 1 GPU" if trainer.args.n_gpu > 0 else "")
        logger.info(
            "No `resources_per_trial` arg was passed into "
            "`hyperparameter_search`. Setting it to a default value "
            f"of {resource_msg} for each trial."
        )
    # Make sure each trainer only uses GPUs that were allocated per trial.
    gpus_per_trial = kwargs["resources_per_trial"].get("gpu", 0)
    trainer.args._n_gpu = gpus_per_trial

    # Setup default `progress_reporter`.
    if "progress_reporter" not in kwargs:
        from ray.tune import CLIReporter

        kwargs["progress_reporter"] = CLIReporter(metric_columns=["objective"])
    if "keep_checkpoints_num" in kwargs and kwargs["keep_checkpoints_num"] > 0:
        # `keep_checkpoints_num=0` would disabled checkpointing
        trainer.use_tune_checkpoints = True
        if kwargs["keep_checkpoints_num"] > 1:
            logger.warning(
                f"Currently keeping {kwargs['keep_checkpoints_num']} checkpoints for each trial. "
                "Checkpoints are usually huge, "
                "consider setting `keep_checkpoints_num=1`."
            )
    if "scheduler" in kwargs:
        from ray.tune.schedulers import ASHAScheduler, HyperBandForBOHB, MedianStoppingRule, PopulationBasedTraining

        # Check if checkpointing is enabled for PopulationBasedTraining
        if isinstance(kwargs["scheduler"], PopulationBasedTraining):
            if not trainer.use_tune_checkpoints:
                logger.warning(
                    "You are using PopulationBasedTraining but you haven't enabled checkpointing. "
                    "This means your trials will train from scratch everytime they are exploiting "
                    "new configurations. Consider enabling checkpointing by passing "
                    "`keep_checkpoints_num=1` as an additional argument to `Trainer.hyperparameter_search`."
                )

        # Check for `do_eval` and `eval_during_training` for schedulers that require intermediate reporting.
        if isinstance(
            kwargs["scheduler"], (ASHAScheduler, MedianStoppingRule, HyperBandForBOHB, PopulationBasedTraining)
        ) and (not trainer.args.do_eval or trainer.args.evaluation_strategy == IntervalStrategy.NO):
            raise RuntimeError(
                "You are using {cls} as a scheduler but you haven't enabled evaluation during training. "
                "This means your trials will not report intermediate results to Ray Tune, and "
                "can thus not be stopped early or used to exploit other trials parameters. "
                "If this is what you want, do not use {cls}. If you would like to use {cls}, "
                "make sure you pass `do_eval=True` and `evaluation_strategy='steps'` in the "
                "Trainer `args`.".format(cls=type(kwargs["scheduler"]).__name__)
            )

    trainable = ray.tune.with_parameters(_objective, local_trainer=trainer)

    @functools.wraps(trainable)
    def dynamic_modules_import_trainable(*args, **kwargs):
        """
        Wrapper around ``tune.with_parameters`` to ensure datasets_modules are loaded on each Actor.

        Without this, an ImportError will be thrown. See https://github.com/huggingface/transformers/issues/11565.

        Assumes that ``_objective``, defined above, is a function.
        """
        if is_datasets_available():
            import datasets.load

            dynamic_modules_path = os.path.join(datasets.load.init_dynamic_modules(), "__init__.py")
            # load dynamic_modules from path
            spec = importlib.util.spec_from_file_location("datasets_modules", dynamic_modules_path)
            datasets_modules = importlib.util.module_from_spec(spec)
            sys.modules[spec.name] = datasets_modules
            spec.loader.exec_module(datasets_modules)
        return trainable(*args, **kwargs)

    # special attr set by tune.with_parameters
    if hasattr(trainable, "__mixins__"):
        dynamic_modules_import_trainable.__mixins__ = trainable.__mixins__

    analysis = ray.tune.run(
        dynamic_modules_import_trainable,
        config=trainer.hp_space(None),
        num_samples=n_trials,
        **kwargs,
    )
    best_trial = analysis.get_best_trial(metric="objective", mode=direction[:3])
    best_run = BestRun(best_trial.trial_id, best_trial.last_result["objective"], best_trial.config)
    if _tb_writer is not None:
        trainer.add_callback(_tb_writer)
    return best_run


def run_hp_search_sigopt(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:

    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


def get_available_reporting_integrations():
    integrations = []
    if is_azureml_available():
        integrations.append("azure_ml")
    if is_comet_available():
        integrations.append("comet_ml")
    if is_mlflow_available():
        integrations.append("mlflow")
    if is_tensorboard_available():
        integrations.append("tensorboard")
    if is_wandb_available():
        integrations.append("wandb")
    if is_codecarbon_available():
        integrations.append("codecarbon")
    return integrations


def rewrite_logs(d):
    new_d = {}
    eval_prefix = "eval_"
    eval_prefix_len = len(eval_prefix)
    test_prefix = "test_"
    test_prefix_len = len(test_prefix)
    for k, v in d.items():
        if k.startswith(eval_prefix):
            new_d["eval/" + k[eval_prefix_len:]] = v
        elif k.startswith(test_prefix):
            new_d["test/" + k[test_prefix_len:]] = v
        else:
            new_d["train/" + k] = v
    return new_d


[docs]class TensorBoardCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `TensorBoard <https://www.tensorflow.org/tensorboard>`__. Args: tb_writer (:obj:`SummaryWriter`, `optional`): The writer to use. Will instantiate one if not set. """ def __init__(self, tb_writer=None): has_tensorboard = is_tensorboard_available() if not has_tensorboard: raise RuntimeError( "TensorBoardCallback requires tensorboard to be installed. Either update your PyTorch version or install tensorboardX." ) if has_tensorboard: try: from torch.utils.tensorboard import SummaryWriter # noqa: F401 self._SummaryWriter = SummaryWriter except ImportError: try: from tensorboardX import SummaryWriter self._SummaryWriter = SummaryWriter except ImportError: self._SummaryWriter = None else: self._SummaryWriter = None self.tb_writer = tb_writer def _init_summary_writer(self, args, log_dir=None): log_dir = log_dir or args.logging_dir if self._SummaryWriter is not None: self.tb_writer = self._SummaryWriter(log_dir=log_dir) def on_train_begin(self, args, state, control, **kwargs): if not state.is_world_process_zero: return log_dir = None if state.is_hyper_param_search: trial_name = state.trial_name if trial_name is not None: log_dir = os.path.join(args.logging_dir, trial_name) if self.tb_writer is None: self._init_summary_writer(args, log_dir) if self.tb_writer is not None: self.tb_writer.add_text("args", args.to_json_string()) if "model" in kwargs: model = kwargs["model"] if hasattr(model, "config") and model.config is not None: model_config_json = model.config.to_json_string() self.tb_writer.add_text("model_config", model_config_json) # Version of TensorBoard coming from tensorboardX does not have this method. if hasattr(self.tb_writer, "add_hparams"): self.tb_writer.add_hparams(args.to_sanitized_dict(), metric_dict={}) def on_log(self, args, state, control, logs=None, **kwargs): if not state.is_world_process_zero: return if self.tb_writer is None: self._init_summary_writer(args) if self.tb_writer is not None: logs = rewrite_logs(logs) for k, v in logs.items(): if isinstance(v, (int, float)): self.tb_writer.add_scalar(k, v, state.global_step) else: logger.warning( "Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a scalar. ' "This invocation of Tensorboard's writer.add_scalar() " "is incorrect so we dropped this attribute." ) self.tb_writer.flush() def on_train_end(self, args, state, control, **kwargs): if self.tb_writer: self.tb_writer.close() self.tb_writer = None
[docs]class WandbCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `Weight and Biases <https://www.wandb.com/>`__. """ def __init__(self): has_wandb = is_wandb_available() if not has_wandb: raise RuntimeError("WandbCallback requires wandb to be installed. Run `pip install wandb`.") if has_wandb: import wandb self._wandb = wandb self._initialized = False # log outputs self._log_model = os.getenv("WANDB_LOG_MODEL", "FALSE").upper() in ENV_VARS_TRUE_VALUES.union({"TRUE"})
[docs] def setup(self, args, state, model, **kwargs): """ Setup the optional Weights & Biases (`wandb`) integration. One can subclass and override this method to customize the setup if needed. Find more information `here <https://docs.wandb.ai/integrations/huggingface>`__. You can also override the following environment variables: Environment: WANDB_LOG_MODEL (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to log model as artifact at the end of training. Use along with `TrainingArguments.load_best_model_at_end` to upload best model. WANDB_WATCH (:obj:`str`, `optional` defaults to :obj:`"gradients"`): Can be :obj:`"gradients"`, :obj:`"all"` or :obj:`"false"`. Set to :obj:`"false"` to disable gradient logging or :obj:`"all"` to log gradients and parameters. WANDB_PROJECT (:obj:`str`, `optional`, defaults to :obj:`"huggingface"`): Set this to a custom string to store results in a different project. WANDB_DISABLED (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to disable wandb entirely. Set `WANDB_DISABLED=true` to disable. """ if self._wandb is None: return self._initialized = True if state.is_world_process_zero: logger.info( 'Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"' ) combined_dict = {**args.to_sanitized_dict()} if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} trial_name = state.trial_name init_args = {} if trial_name is not None: run_name = trial_name init_args["group"] = args.run_name else: run_name = args.run_name if self._wandb.run is None: self._wandb.init( project=os.getenv("WANDB_PROJECT", "huggingface"), name=run_name, **init_args, ) # add config parameters (run may have been created manually) self._wandb.config.update(combined_dict, allow_val_change=True) # define default x-axis (for latest wandb versions) if getattr(self._wandb, "define_metric", None): self._wandb.define_metric("train/global_step") self._wandb.define_metric("*", step_metric="train/global_step", step_sync=True) # keep track of model topology and gradients, unsupported on TPU if not is_torch_tpu_available() and os.getenv("WANDB_WATCH") != "false": self._wandb.watch( model, log=os.getenv("WANDB_WATCH", "gradients"), log_freq=max(100, args.logging_steps) )
def on_train_begin(self, args, state, control, model=None, **kwargs): if self._wandb is None: return hp_search = state.is_hyper_param_search if hp_search: self._wandb.finish() self._initialized = False if not self._initialized: self.setup(args, state, model, **kwargs) def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs): if self._wandb is None: return if self._log_model and self._initialized and state.is_world_process_zero: from .trainer import Trainer fake_trainer = Trainer(args=args, model=model, tokenizer=tokenizer) with tempfile.TemporaryDirectory() as temp_dir: fake_trainer.save_model(temp_dir) metadata = ( { k: v for k, v in dict(self._wandb.summary).items() if isinstance(v, numbers.Number) and not k.startswith("_") } if not args.load_best_model_at_end else { f"eval/{args.metric_for_best_model}": state.best_metric, "train/total_floss": state.total_flos, } ) artifact = self._wandb.Artifact(name=f"model-{self._wandb.run.id}", type="model", metadata=metadata) for f in Path(temp_dir).glob("*"): if f.is_file(): with artifact.new_file(f.name, mode="wb") as fa: fa.write(f.read_bytes()) self._wandb.run.log_artifact(artifact) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if self._wandb is None: return if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: logs = rewrite_logs(logs) self._wandb.log({**logs, "train/global_step": state.global_step})
[docs]class CometCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `Comet ML <https://www.comet.ml/site/>`__. """ def __init__(self): if not _has_comet: raise RuntimeError("CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.") self._initialized = False
[docs] def setup(self, args, state, model): """ Setup the optional Comet.ml integration. Environment: COMET_MODE (:obj:`str`, `optional`): "OFFLINE", "ONLINE", or "DISABLED" COMET_PROJECT_NAME (:obj:`str`, `optional`): Comet.ml project name for experiments COMET_OFFLINE_DIRECTORY (:obj:`str`, `optional`): Folder to use for saving offline experiments when :obj:`COMET_MODE` is "OFFLINE" For a number of configurable items in the environment, see `here <https://www.comet.ml/docs/python-sdk/advanced/#comet-configuration-variables>`__. """ self._initialized = True if state.is_world_process_zero: comet_mode = os.getenv("COMET_MODE", "ONLINE").upper() args = {"project_name": os.getenv("COMET_PROJECT_NAME", "huggingface")} experiment = None if comet_mode == "ONLINE": experiment = comet_ml.Experiment(**args) logger.info("Automatic Comet.ml online logging enabled") elif comet_mode == "OFFLINE": args["offline_directory"] = os.getenv("COMET_OFFLINE_DIRECTORY", "./") experiment = comet_ml.OfflineExperiment(**args) logger.info("Automatic Comet.ml offline logging enabled; use `comet upload` when finished") if experiment is not None: experiment._set_model_graph(model, framework="transformers") experiment._log_parameters(args, prefix="args/", framework="transformers") if hasattr(model, "config"): experiment._log_parameters(model.config, prefix="config/", framework="transformers")
def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, model=None, logs=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: experiment = comet_ml.config.get_global_experiment() if experiment is not None: experiment._log_metrics(logs, step=state.global_step, epoch=state.epoch, framework="transformers")
[docs]class AzureMLCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `AzureML <https://pypi.org/project/azureml-sdk/>`__. """ def __init__(self, azureml_run=None): if not is_azureml_available(): raise RuntimeError("AzureMLCallback requires azureml to be installed. Run `pip install azureml-sdk`.") self.azureml_run = azureml_run def on_init_end(self, args, state, control, **kwargs): from azureml.core.run import Run if self.azureml_run is None and state.is_world_process_zero: self.azureml_run = Run.get_context() def on_log(self, args, state, control, logs=None, **kwargs): if self.azureml_run and state.is_world_process_zero: for k, v in logs.items(): if isinstance(v, (int, float)): self.azureml_run.log(k, v, description=k)
[docs]class MLflowCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `MLflow <https://www.mlflow.org/>`__. """ def __init__(self): if not is_mlflow_available(): raise RuntimeError("MLflowCallback requires mlflow to be installed. Run `pip install mlflow`.") import mlflow self._MAX_PARAM_VAL_LENGTH = mlflow.utils.validation.MAX_PARAM_VAL_LENGTH self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH self._initialized = False self._log_artifacts = False self._ml_flow = mlflow
[docs] def setup(self, args, state, model): """ Setup the optional MLflow integration. Environment: HF_MLFLOW_LOG_ARTIFACTS (:obj:`str`, `optional`): Whether to use MLflow .log_artifact() facility to log artifacts. This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `1`, will copy whatever is in :class:`~transformers.TrainingArguments`'s ``output_dir`` to the local or remote artifact storage. Using it without a remote storage will just copy the files to your artifact location. """ log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() if log_artifacts in {"TRUE", "1"}: self._log_artifacts = True if state.is_world_process_zero: self._ml_flow.start_run() combined_dict = args.to_dict() if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} # remove params that are too long for MLflow for name, value in list(combined_dict.items()): # internally, all values are converted to str in MLflow if len(str(value)) > self._MAX_PARAM_VAL_LENGTH: logger.warning( f"Trainer is attempting to log a value of " f'"{value}" for key "{name}" as a parameter. ' f"MLflow's log_param() only accepts values no longer than " f"250 characters so we dropped this attribute." ) del combined_dict[name] # MLflow cannot log more than 100 values in one go, so we have to split it combined_dict_items = list(combined_dict.items()) for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH): self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH])) self._initialized = True
def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, logs, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: metrics = {} for k, v in logs.items(): if isinstance(v, (int, float)): metrics[k] = v else: logger.warning( f"Trainer is attempting to log a value of " f'"{v}" of type {type(v)} for key "{k}" as a metric. ' f"MLflow's log_metric() only accepts float and " f"int types so we dropped this attribute." ) self._ml_flow.log_metrics(metrics=metrics, step=state.global_step) def on_train_end(self, args, state, control, **kwargs): if self._initialized and state.is_world_process_zero: if self._log_artifacts: logger.info("Logging artifacts. This may take time.") self._ml_flow.log_artifacts(args.output_dir) def __del__(self): # if the previous run is not terminated correctly, the fluent API will # not let you start a new run before the previous one is killed if self._ml_flow.active_run is not None: self._ml_flow.end_run()
class NeptuneCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that sends the logs to `Neptune <https://neptune.ai>`. """ def __init__(self): if not is_neptune_available(): raise ValueError( "NeptuneCallback requires neptune-client to be installed. Run `pip install neptune-client`." ) import neptune.new as neptune self._neptune = neptune self._initialized = False self._log_artifacts = False def setup(self, args, state, model): """ Setup the Neptune integration. Environment: NEPTUNE_PROJECT (:obj:`str`, `required`): The project ID for neptune.ai account. Should be in format `workspace_name/project_name` NEPTUNE_API_TOKEN (:obj:`str`, `required`): API-token for neptune.ai account NEPTUNE_CONNECTION_MODE (:obj:`str`, `optional`): Neptune connection mode. `async` by default NEPTUNE_RUN_NAME (:obj:`str`, `optional`): The name of run process on Neptune dashboard """ if state.is_world_process_zero: self._neptune_run = self._neptune.init( project=os.getenv("NEPTUNE_PROJECT"), api_token=os.getenv("NEPTUNE_API_TOKEN"), mode=os.getenv("NEPTUNE_CONNECTION_MODE", "async"), name=os.getenv("NEPTUNE_RUN_NAME", None), run=os.getenv("NEPTUNE_RUN_ID", None), ) combined_dict = args.to_dict() if hasattr(model, "config") and model.config is not None: model_config = model.config.to_dict() combined_dict = {**model_config, **combined_dict} self._neptune_run["parameters"] = combined_dict self._initialized = True def on_train_begin(self, args, state, control, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) def on_log(self, args, state, control, logs, model=None, **kwargs): if not self._initialized: self.setup(args, state, model) if state.is_world_process_zero: for k, v in logs.items(): self._neptune_run[k].log(v, step=state.global_step) def __del__(self): """ Environment: NEPTUNE_STOP_TIMEOUT (:obj:`int`, `optional`): Number of seconsds to wait for all Neptune.ai tracking calls to finish, before stopping the tracked run. If not set it will wait for all tracking calls to finish. """ try: stop_timeout = os.getenv("NEPTUNE_STOP_TIMEOUT") stop_timeout = int(stop_timeout) if stop_timeout else None self._neptune_run.stop(seconds=stop_timeout) except AttributeError: pass class CodeCarbonCallback(TrainerCallback): """ A :class:`~transformers.TrainerCallback` that tracks the CO2 emission of training. """ def __init__(self): if not is_codecarbon_available(): raise RuntimeError( "CodeCarbonCallback requires `codecarbon` to be installed. Run `pip install codecarbon`." ) import codecarbon self._codecarbon = codecarbon self.tracker = None def on_init_end(self, args, state, control, **kwargs): if self.tracker is None and state.is_local_process_zero: # CodeCarbon will automatically handle environment variables for configuration self.tracker = self._codecarbon.EmissionsTracker(output_dir=args.output_dir) def on_train_begin(self, args, state, control, model=None, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.start() def on_train_end(self, args, state, control, **kwargs): if self.tracker and state.is_local_process_zero: self.tracker.stop() INTEGRATION_TO_CALLBACK = { "azure_ml": AzureMLCallback, "comet_ml": CometCallback, "mlflow": MLflowCallback, "neptune": NeptuneCallback, "tensorboard": TensorBoardCallback, "wandb": WandbCallback, "codecarbon": CodeCarbonCallback, } def get_reporting_integration_callbacks(report_to): for integration in report_to: if integration not in INTEGRATION_TO_CALLBACK: raise ValueError( f"{integration} is not supported, only {', '.join(INTEGRATION_TO_CALLBACK.keys())} are supported." ) return [INTEGRATION_TO_CALLBACK[integration] for integration in report_to]