# 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 importlib.util
import io
import json
import numbers
import os
import re
import tempfile
from pathlib import Path
from types import SimpleNamespace
from .trainer_utils import SchedulerType
from .utils import logging
from .utils.versions import require_version
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 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.warn(
"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_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_deepspeed_available():
return importlib.util.find_spec("deepspeed") 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
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"
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):
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)
# 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_checkpoint_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__)
)
analysis = ray.tune.run(
ray.tune.with_parameters(_objective, local_trainer=trainer),
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 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")
return integrations
def rewrite_logs(d):
new_d = {}
eval_prefix = "eval_"
eval_prefix_len = len(eval_prefix)
for k, v in d.items():
if k.startswith(eval_prefix):
new_d["eval/" + k[eval_prefix_len:]] = v
else:
new_d["train/" + k] = v
return new_d
def init_deepspeed(trainer, num_training_steps):
"""
Init DeepSpeed, after converting any relevant Trainer's args into DeepSpeed configuration
Args:
trainer: Trainer object
num_training_steps: per single gpu
Returns: model, optimizer, lr_scheduler
"""
import deepspeed
require_version("deepspeed>0.3.10")
args = trainer.args
ds_config_file = args.deepspeed
model = trainer.model
with io.open(ds_config_file, "r", encoding="utf-8") as f:
config = json.load(f)
# The following code translates relevant trainer's cl args into the DS config
# First to ensure that there is no mismatch between cl args values and presets in the config
# file, ask to not set in ds config file:
# - "train_batch_size",
# - "train_micro_batch_size_per_gpu",
# - "gradient_accumulation_steps"
bs_keys = ["train_batch_size", "train_micro_batch_size_per_gpu"]
if len([x for x in bs_keys if x in config.keys()]):
raise ValueError(
f"Do not include {bs_keys} entries in the ds config file, as they will be set via --per_device_train_batch_size or its default"
)
if "gradient_accumulation_steps" in config.keys():
raise ValueError(
"Do not include gradient_accumulation_steps entries in the ds config file, as they will be set via --gradient_accumulation_steps or its default"
)
# DeepSpeed does:
# train_batch_size = n_gpus * train_micro_batch_size_per_gpu * gradient_accumulation_steps
# therefore we just need to set:
config["train_micro_batch_size_per_gpu"] = args.per_device_train_batch_size
config["gradient_accumulation_steps"] = args.gradient_accumulation_steps
if "gradient_clipping" in config:
logger.info(
f"Keeping the `gradient_clipping` config from {ds_config_file} intact, ignoring any gradient clipping-specific cl args"
)
else: # override only if the ds config doesn't already have this section
config["gradient_clipping"] = args.max_grad_norm
if "optimizer" in config:
logger.info(
f"Keeping the `optimizer` config from {ds_config_file} intact, ignoring any optimizer-specific cl args"
)
else: # override only if the ds config doesn't already have this section
# ds supports Adam, AdamW, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
# To use other optimizers requires voiding warranty with: `"zero_allow_untested_optimizer": true"`
optimizer_configs = {
"AdamW": {
"lr": args.learning_rate,
"betas": [args.adam_beta1, args.adam_beta2],
"eps": args.adam_epsilon,
"weight_decay": args.weight_decay,
}
}
optimizer = "AdamW"
config["optimizer"] = {
"type": optimizer,
"params": optimizer_configs[optimizer],
}
# DS schedulers (deepspeed/runtime/lr_schedules.py):
#
# DS name | --lr_scheduler_type | HF func | Notes
# -------------| ---------------------|-----------------------------------|--------------------
# LRRangeTest | na | na | LRRT
# OneCycle | na | na | 1CLR
# WarmupLR | constant_with_warmup | get_constant_schedule_with_warmup | w/ warmup_min_lr=0
# WarmupDecayLR| linear | get_linear_schedule_with_warmup |
if "scheduler" in config:
logger.info(
f"Keeping the `scheduler` config from {ds_config_file} intact, ignoring any scheduler-specific cl args"
)
else: # override only if the ds config doesn't already have this section
if args.lr_scheduler_type == SchedulerType.LINEAR:
scheduler = "WarmupDecayLR"
params = {
"last_batch_iteration": -1,
"total_num_steps": num_training_steps,
"warmup_min_lr": 0,
"warmup_max_lr": args.learning_rate,
"warmup_num_steps": args.warmup_steps,
}
elif args.lr_scheduler_type == SchedulerType.CONSTANT_WITH_WARMUP:
scheduler = "WarmupLR"
params = {
"warmup_min_lr": 0,
"warmup_max_lr": args.learning_rate,
"warmup_num_steps": args.warmup_steps,
}
else:
raise ValueError(f"{args.lr_scheduler_type} scheduler type is not supported by DeepSpeed")
config["scheduler"] = {
"type": scheduler,
"params": params,
}
# fp16
if trainer.fp16_backend is not None:
# Deepspeed has 2 possible fp16 config entries:
# - `fp16`: for the native amp - it has a bunch of optional params but we won't set any here unless the user did the work
# - `amp`: which delegates amp work to apex (which needs to be available), but it cannot be used with any ZeRO features, so probably best to be avoided.
if trainer.fp16_backend == "apex":
if "amp" in config:
logger.info(
f"Keeping the `amp` config from {ds_config_file} intact, ignoring any amp-specific cl args"
)
else:
config["amp"] = {
"enabled": True,
"opt_level": args.fp16_opt_level,
}
elif trainer.fp16_backend == "amp":
if "fp16" in config:
logger.info(
f"Keeping the `fp16` config from {ds_config_file} intact, ignoring any fp16-specific cl args"
)
else:
config["fp16"] = {
"enabled": True,
}
# for clarity extract the specific cl args that are being passed to deepspeed
ds_args = dict(local_rank=args.local_rank)
# init that takes part of the config via `args`, and the bulk of it via `config_params`
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
model, optimizer, _, lr_scheduler = deepspeed.initialize(
args=SimpleNamespace(**ds_args), # expects an obj
model=model,
model_parameters=model_parameters,
config_params=config,
)
return model, optimizer, lr_scheduler
[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()
assert (
has_tensorboard
), "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)
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 state.is_world_process_zero:
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 "
'"%s" of type %s for key "%s" as a scalar. '
"This invocation of Tensorboard's writer.add_scalar() "
"is incorrect so we dropped this attribute.",
v,
type(v),
k,
)
self.tb_writer.flush()
def on_train_end(self, args, state, control, **kwargs):
if self.tb_writer:
self.tb_writer.close()
[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()
assert has_wandb, "WandbCallback requires wandb to be installed. Run `pip install wandb`."
if has_wandb:
import wandb
wandb.ensure_configured()
if wandb.api.api_key is None:
has_wandb = False
logger.warning(
"W&B installed but not logged in. Run `wandb login` or set the WANDB_API_KEY env variable."
)
self._wandb = None
else:
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, reinit, **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.
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
self._wandb.init(
project=os.getenv("WANDB_PROJECT", "huggingface"),
config=combined_dict,
name=run_name,
reinit=reinit,
**init_args,
)
# 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 not self._initialized or hp_search:
self.setup(args, state, model, reinit=hp_search, **kwargs)
def on_train_end(self, args, state, control, model=None, tokenizer=None, **kwargs):
if self._wandb is None:
return
# commit last step
if state.is_world_process_zero:
self._wandb.log({})
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)
# use run name and ensure it's a valid Artifact name
artifact_name = re.sub(r"[^a-zA-Z0-9_\.\-]", "", self._wandb.run.name)
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"run-{artifact_name}", 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, reinit=False)
if state.is_world_process_zero:
logs = rewrite_logs(logs)
self._wandb.log(logs, 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):
assert _has_comet, "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):
assert (
is_azureml_available()
), "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:
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):
assert is_mlflow_available(), "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 TrainerArgument'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:
for k, v in logs.items():
if isinstance(v, (int, float)):
self._ml_flow.log_metric(k, v, step=state.global_step)
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."
)
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()
INTEGRATION_TO_CALLBACK = {
"azure_ml": AzureMLCallback,
"comet_ml": CometCallback,
"mlflow": MLflowCallback,
"tensorboard": TensorBoardCallback,
"wandb": WandbCallback,
}
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]