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# coding=utf-8
# Copyright 2023 Huawei Technologies Co., Ltd
#
# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# limitations under the License.
# Copyright 2020 The HuggingFace Team. All rights reserved.
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# 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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import contextlib
import io
import json
import math
import os
import warnings
from dataclasses import asdict, dataclass, field, fields
from datetime import timedelta
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
from packaging import version
from .debug_utils import DebugOption
from .trainer_utils import (
EvaluationStrategy,
FSDPOption,
HubStrategy,
IntervalStrategy,
SchedulerType,
ShardedDDPOption,
)
from .utils import (
ExplicitEnum,
cached_property,
ccl_version,
get_full_repo_name,
is_accelerate_available,
is_psutil_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_torch_available,
is_torch_bf16_cpu_available,
is_torch_bf16_gpu_available,
is_torch_neuroncore_available,
is_torch_tf32_available,
is_torch_tpu_available,
logging,
requires_backends,
)
if is_torch_available():
import torch
import torch.distributed as dist
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
if is_torch_neuroncore_available(check_device=False):
# torchrun support
# https://github.com/pytorch/xla/pull/3609
if os.environ.get("TORCHELASTIC_RUN_ID"):
import torch_xla.distributed.xla_backend as xbn
if not isinstance(torch.distributed.group.WORLD, xbn.ProcessGroupXla):
torch.distributed.init_process_group(backend="xla")
if not isinstance(torch.distributed.group.WORLD, xbn.ProcessGroupXla):
raise AssertionError("Failed to initialize torch.distributed process group using XLA backend.")
if is_sagemaker_mp_enabled():
import smdistributed.modelparallel.torch as smp
smp.init()
logger = logging.get_logger(__name__)
log_levels = logging.get_log_levels_dict().copy()
trainer_log_levels = dict(**log_levels, passive=-1)
def default_logdir() -> str:
"""
Same default as PyTorch
"""
import socket
from datetime import datetime
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
return os.path.join("runs", current_time + "_" + socket.gethostname())
def get_int_from_env(env_keys, default):
"""Returns the first positive env value found in the `env_keys` list or the default."""
for e in env_keys:
val = int(os.environ.get(e, -1))
if val >= 0:
return val
return default
def get_xla_device_type(device: "torch.device") -> Optional[str]:
"""
Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device.
"""
if is_torch_tpu_available():
return xm.xla_real_devices([device])[0].split(":")[0]
return None
class OptimizerNames(ExplicitEnum):
"""
Stores the acceptable string identifiers for optimizers.
"""
ADAMW_HF = "adamw_hf"
ADAMW_TORCH = "adamw_torch"
ADAMW_TORCH_FUSED = "adamw_torch_fused"
ADAMW_TORCH_XLA = "adamw_torch_xla"
ADAMW_APEX_FUSED = "adamw_apex_fused"
ADAFACTOR = "adafactor"
ADAMW_BNB = "adamw_bnb_8bit"
ADAMW_ANYPRECISION = "adamw_anyprecision"
SGD = "sgd"
ADAGRAD = "adagrad"
@dataclass
class TrainingArguments:
"""
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
output_dir (`str`):
The output directory where the model predictions and checkpoints will be written.
overwrite_output_dir (`bool`, *optional*, defaults to `False`):
If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir`
points to a checkpoint directory.
do_train (`bool`, *optional*, defaults to `False`):
Whether to run training or not. This argument is not directly used by [`Trainer`], it's intended to be used
by your training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
do_eval (`bool`, *optional*):
Whether to run evaluation on the validation set or not. Will be set to `True` if `evaluation_strategy` is
different from `"no"`. This argument is not directly used by [`Trainer`], it's intended to be used by your
training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
do_predict (`bool`, *optional*, defaults to `False`):
Whether to run predictions on the test set or not. This argument is not directly used by [`Trainer`], it's
intended to be used by your training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
evaluation_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
The evaluation strategy to adopt during training. Possible values are:
- `"no"`: No evaluation is done during training.
- `"steps"`: Evaluation is done (and logged) every `eval_steps`.
- `"epoch"`: Evaluation is done at the end of each epoch.
prediction_loss_only (`bool`, *optional*, defaults to `False`):
When performing evaluation and generating predictions, only returns the loss.
per_device_train_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/TPU core/CPU for training.
per_device_eval_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/TPU core/CPU for evaluation.
gradient_accumulation_steps (`int`, *optional*, defaults to 1):
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
<Tip warning={true}>
When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging,
evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training examples.
</Tip>
eval_accumulation_steps (`int`, *optional*):
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but
requires more memory).
eval_delay (`float`, *optional*):
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
evaluation_strategy.
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for [`AdamW`] optimizer.
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in [`AdamW`]
optimizer.
adam_beta1 (`float`, *optional*, defaults to 0.9):
The beta1 hyperparameter for the [`AdamW`] optimizer.
adam_beta2 (`float`, *optional*, defaults to 0.999):
The beta2 hyperparameter for the [`AdamW`] optimizer.
adam_epsilon (`float`, *optional*, defaults to 1e-8):
The epsilon hyperparameter for the [`AdamW`] optimizer.
max_grad_norm (`float`, *optional*, defaults to 1.0):
Maximum gradient norm (for gradient clipping).
num_train_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform (if not an integer, will perform the decimal part percents of
the last epoch before stopping training).
max_steps (`int`, *optional*, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides `num_train_epochs`.
In case of using a finite iterable dataset the training may stop before reaching the set number of steps
when all data is exhausted
lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
warmup_ratio (`float`, *optional*, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
warmup_steps (`int`, *optional*, defaults to 0):
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
log_level (`str`, *optional*, defaults to `passive`):
Logger log level to use on the main process. Possible choices are the log levels as strings: 'debug',
'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and keeps the
current log level for the Transformers library (which will be `"warning"` by default).
log_level_replica (`str`, *optional*, defaults to `"warning"`):
Logger log level to use on replicas. Same choices as `log_level`"
log_on_each_node (`bool`, *optional*, defaults to `True`):
In multinode distributed training, whether to log using `log_level` once per node, or only on the main
node.
logging_dir (`str`, *optional*):
[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to
*output_dir/runs/**CURRENT_DATETIME_HOSTNAME***.
logging_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The logging strategy to adopt during training. Possible values are:
- `"no"`: No logging is done during training.
- `"epoch"`: Logging is done at the end of each epoch.
- `"steps"`: Logging is done every `logging_steps`.
logging_first_step (`bool`, *optional*, defaults to `False`):
Whether to log and evaluate the first `global_step` or not.
logging_steps (`int`, *optional*, defaults to 500):
Number of update steps between two logs if `logging_strategy="steps"`.
logging_nan_inf_filter (`bool`, *optional*, defaults to `True`):
Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan`
or `inf` is filtered and the average loss of the current logging window is taken instead.
<Tip>
`logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.
</Tip>
save_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The checkpoint save strategy to adopt during training. Possible values are:
- `"no"`: No save is done during training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`.
save_steps (`int`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
save_total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
`output_dir`.
save_on_each_node (`bool`, *optional*, defaults to `False`):
When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on
the main one.
This should not be activated when the different nodes use the same storage as the files will be saved with
the same names for each node.
no_cuda (`bool`, *optional*, defaults to `False`):
Whether to not use CUDA even when it is available or not.
seed (`int`, *optional*, defaults to 42):
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the
[`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized parameters.
data_seed (`int`, *optional*):
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model
seed.
jit_mode_eval (`bool`, *optional*, defaults to `False`):
Whether or not to use PyTorch jit trace for inference.
use_ipex (`bool`, *optional*, defaults to `False`):
Use Intel extension for PyTorch when it is available. [IPEX
installation](https://github.com/intel/intel-extension-for-pytorch).
bf16 (`bool`, *optional*, defaults to `False`):
Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher
NVIDIA architecture or using CPU (no_cuda). This is an experimental API and it may change.
fp16 (`bool`, *optional*, defaults to `False`):
Whether to use fp16 16-bit (mixed) precision training instead of 32-bit training.
fp16_opt_level (`str`, *optional*, defaults to 'O1'):
For `fp16` training, Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. See details on
the [Apex documentation](https://nvidia.github.io/apex/amp).
fp16_backend (`str`, *optional*, defaults to `"auto"`):
This argument is deprecated. Use `half_precision_backend` instead.
half_precision_backend (`str`, *optional*, defaults to `"auto"`):
The backend to use for mixed precision training. Must be one of `"auto", "cuda_amp", "apex", "cpu_amp"`.
`"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices
will force the requested backend.
bf16_full_eval (`bool`, *optional*, defaults to `False`):
Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values. This is an experimental API and it may change.
fp16_full_eval (`bool`, *optional*, defaults to `False`):
Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm
metric values.
tf32 (`bool`, *optional*):
Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends
on PyTorch's version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to
the [TF32](https://huggingface.co/docs/transformers/performance#tf32) documentation. This is an
experimental API and it may change.
local_rank (`int`, *optional*, defaults to -1):
Rank of the process during distributed training.
xpu_backend (`str`, *optional*):
The backend to use for xpu distributed training. Must be one of `"mpi"` or `"ccl"` or `"gloo"`.
tpu_num_cores (`int`, *optional*):
When training on TPU, the number of TPU cores (automatically passed by launcher script).
dataloader_drop_last (`bool`, *optional*, defaults to `False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size)
or not.
eval_steps (`int`, *optional*):
Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same
value as `logging_steps` if not set.
dataloader_num_workers (`int`, *optional*, defaults to 0):
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the
main process.
past_index (`int`, *optional*, defaults to -1):
Some models like [TransformerXL](../model_doc/transformerxl) or [XLNet](../model_doc/xlnet) can make use of
the past hidden states for their predictions. If this argument is set to a positive int, the `Trainer` will
use the corresponding output (usually index 2) as the past state and feed it to the model at the next
training step under the keyword argument `mems`.
run_name (`str`, *optional*):
A descriptor for the run. Typically used for [wandb](https://www.wandb.com/) and
[mlflow](https://www.mlflow.org/) logging.
disable_tqdm (`bool`, *optional*):
Whether or not to disable the tqdm progress bars and table of metrics produced by
[`~notebook.NotebookTrainingTracker`] in Jupyter Notebooks. Will default to `True` if the logging level is
set to warn or lower (default), `False` otherwise.
remove_unused_columns (`bool`, *optional*, defaults to `True`):
Whether or not to automatically remove the columns unused by the model forward method.
(Note that this behavior is not implemented for [`TFTrainer`] yet.)
label_names (`List[str]`, *optional*):
The list of keys in your dictionary of inputs that correspond to the labels.
Will eventually default to the list of argument names accepted by the model that contain the word "label",
except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the
`["start_positions", "end_positions"]` keys.
load_best_model_at_end (`bool`, *optional*, defaults to `False`):
Whether or not to load the best model found during training at the end of training.
<Tip>
When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in
the case it is "steps", `save_steps` must be a round multiple of `eval_steps`.
</Tip>
metric_for_best_model (`str`, *optional*):
Use in conjunction with `load_best_model_at_end` to specify the metric to use to compare two different
models. Must be the name of a metric returned by the evaluation with or without the prefix `"eval_"`. Will
default to `"loss"` if unspecified and `load_best_model_at_end=True` (to use the evaluation loss).
If you set this value, `greater_is_better` will default to `True`. Don't forget to set it to `False` if
your metric is better when lower.
greater_is_better (`bool`, *optional*):
Use in conjunction with `load_best_model_at_end` and `metric_for_best_model` to specify if better models
should have a greater metric or not. Will default to:
- `True` if `metric_for_best_model` is set to a value that isn't `"loss"` or `"eval_loss"`.
- `False` if `metric_for_best_model` is not set, or set to `"loss"` or `"eval_loss"`.
ignore_data_skip (`bool`, *optional*, defaults to `False`):
When resuming training, whether or not to skip the epochs and batches to get the data loading at the same
stage as in the previous training. If set to `True`, the training will begin faster (as that skipping step
can take a long time) but will not yield the same results as the interrupted training would have.
sharded_ddp (`bool`, `str` or list of [`~trainer_utils.ShardedDDPOption`], *optional*, defaults to `False`):
Use Sharded DDP training from [FairScale](https://github.com/facebookresearch/fairscale) (in distributed
training only). This is an experimental feature.
A list of options along the following:
- `"simple"`: to use first instance of sharded DDP released by fairscale (`ShardedDDP`) similar to ZeRO-2.
- `"zero_dp_2"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in
Zero-2 mode (with `reshard_after_forward=False`).
- `"zero_dp_3"`: to use the second instance of sharded DPP released by fairscale (`FullyShardedDDP`) in
Zero-3 mode (with `reshard_after_forward=True`).
- `"offload"`: to add ZeRO-offload (only compatible with `"zero_dp_2"` and `"zero_dp_3"`).
If a string is passed, it will be split on space. If a bool is passed, it will be converted to an empty
list for `False` and `["simple"]` for `True`.
fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `False`):
Use PyTorch Distributed Parallel Training (in distributed training only).
A list of options along the following:
- `"full_shard"`: Shard parameters, gradients and optimizer states.
- `"shard_grad_op"`: Shard optimizer states and gradients.
- `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and
`"shard_grad_op"`).
- `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
fsdp_config (`str` or `dict`, *optional*):
Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of
deepspeed json config file (e.g., `ds_config.json`) or an already loaded json file as `dict`.
A List of config and its options:
- fsdp_min_num_params (`int`, *optional*, defaults to `0`):
FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
passed).
- fsdp_transformer_layer_cls_to_wrap (`List[str]`, *optional*):
List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
`T5Block` .... (useful only when `fsdp` flag is passed).
- fsdp_backward_prefetch (`str`, *optional*)
FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
`fsdp` field is passed).
A list of options along the following:
- `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's
gradient
computation.
- `"backward_pos"` : This prefetches the next set of parameters after the current set of
parameter鈥檚
gradient computation.
- fsdp_forward_prefetch (`bool`, *optional*, defaults to `False`)
FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
forward pass.
- limit_all_gathers (`bool`, *optional*, defaults to `False`)
FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
all-gathers.
- xla (`bool`, *optional*, defaults to `False`):
Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
and its API may evolve in the future.
- xla_fsdp_settings (`dict`, *optional*)
The value is a dictionary which stores the XLA FSDP wrapping parameters.
For a complete list of options, please see [here](
https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
- xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`):
Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be
used when the xla flag is set to true, and an auto wrapping policy is specified through
fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
deepspeed (`str` or `dict`, *optional*):
Use [Deepspeed](https://github.com/microsoft/deepspeed). This is an experimental feature and its API may
evolve in the future. The value is either the location of DeepSpeed json config file (e.g.,
`ds_config.json`) or an already loaded json file as a `dict`"
label_smoothing_factor (`float`, *optional*, defaults to 0.0):
The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded
labels are changed from 0s and 1s to `label_smoothing_factor/num_labels` and `1 - label_smoothing_factor +
label_smoothing_factor/num_labels` respectively.
debug (`str` or list of [`~debug_utils.DebugOption`], *optional*, defaults to `""`):
Enable one or more debug features. This is an experimental feature.
Possible options are:
- `"underflow_overflow"`: detects overflow in model's input/outputs and reports the last frames that led to
the event
- `"tpu_metrics_debug"`: print debug metrics on TPU
The options should be separated by whitespaces.
optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_hf"`):
The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or
adafactor.
optim_args (`str`, *optional*):
Optional arguments that are supplied to AnyPrecisionAdamW.
group_by_length (`bool`, *optional*, defaults to `False`):
Whether or not to group together samples of roughly the same length in the training dataset (to minimize
padding applied and be more efficient). Only useful if applying dynamic padding.
length_column_name (`str`, *optional*, defaults to `"length"`):
Column name for precomputed lengths. If the column exists, grouping by length will use these values rather
than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an
instance of `Dataset`.
report_to (`str` or `List[str]`, *optional*, defaults to `"all"`):
The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report to
all integrations installed, `"none"` for no integrations.
ddp_find_unused_parameters (`bool`, *optional*):
When using distributed training, the value of the flag `find_unused_parameters` passed to
`DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
ddp_bucket_cap_mb (`int`, *optional*):
When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`.
dataloader_pin_memory (`bool`, *optional*, defaults to `True`):
Whether you want to pin memory in data loaders or not. Will default to `True`.
skip_memory_metrics (`bool`, *optional*, defaults to `True`):
Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
down the training and evaluation speed.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push the model to the Hub every time the model is saved. If this is activated,
`output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content
will be pushed each time a save is triggered (depending on your `save_strategy`). Calling
[`~Trainer.save_model`] will also trigger a push.
<Tip warning={true}>
If `output_dir` exists, it needs to be a local clone of the repository to which the [`Trainer`] will be
pushed.
</Tip>
resume_from_checkpoint (`str`, *optional*):
The path to a folder with a valid checkpoint for your model. This argument is not directly used by
[`Trainer`], it's intended to be used by your training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
hub_model_id (`str`, *optional*):
The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository name,
for instance `"user_name/model"`, which allows you to push to an organization you are a member of with
`"organization_name/model"`. Will default to `user_name/output_dir_name` with *output_dir_name* being the
name of `output_dir`.
Will default to the name of `output_dir`.
hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`):
Defines the scope of what is pushed to the Hub and when. Possible values are:
- `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a
draft of a model card when the [`~Trainer.save_model`] method is called.
- `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.
- `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
`trainer.train(resume_from_checkpoint="last-checkpoint")`.
- `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output
folder (so you will get one checkpoint folder per folder in your final repository)
hub_token (`str`, *optional*):
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
`huggingface-cli login`.
hub_private_repo (`bool`, *optional*, defaults to `False`):
If True, the Hub repo will be set to private.
gradient_checkpointing (`bool`, *optional*, defaults to `False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
include_inputs_for_metrics (`bool`, *optional*, defaults to `False`):
Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics
that need inputs, predictions and references for scoring calculation in Metric class.
auto_find_batch_size (`bool`, *optional*, defaults to `False`)
Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding
CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
full_determinism (`bool`, *optional*, defaults to `False`)
If `True`, [`enable_full_determinism`] is called instead of [`set_seed`] to ensure reproducible results in
distributed training
torchdynamo (`str`, *optional*):
If set, the backend compiler for TorchDynamo. Possible choices are `"eager"`, `"aot_eager"`, `"inductor"`,
`"nvfuser"`, `"aot_nvfuser"`, `"aot_cudagraphs"`, `"ofi"`, `"fx2trt"`, `"onnxrt"` and `"ipex"`.
ray_scope (`str`, *optional*, defaults to `"last"`):
The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will
then use the last checkpoint of all trials, compare those, and select the best one. However, other options
are also available. See the [Ray documentation](
https://docs.ray.io/en/latest/tune/api_docs/analysis.html#ray.tune.ExperimentAnalysis.get_best_trial) for
more options.
ddp_timeout (`int`, *optional*, defaults to 1800):
The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when
performing slow operations in distributed runnings. Please refer the [PyTorch documentation]
(https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more
information.
use_mps_device (`bool`, *optional*, defaults to `False`):
Whether to use Apple Silicon chip based `mps` device.
torch_compile (`bool`, *optional*, defaults to `False`):
Whether or not to compile the model using PyTorch 2.0
[`torch.compile`](https://pytorch.org/get-started/pytorch-2.0/) (requires a nighlty install of PyTorch).
This will use the best defaults for the [`torch.compile`
API](https://pytorch.org/docs/2.0/generated/torch.compile.html?highlight=torch+compile#torch.compile). You
can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we don't
guarantee any of them will work as the support is progressively rolled in in PyTorch.
This flag and the whole compile API is experimental and subject to change in future releases.
torch_compile_backend (`str`, *optional*):
The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.
Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.
This flag is experimental and subject to change in future releases.
torch_compile_mode (`str`, *optional*):
The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.
Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.
This flag is experimental and subject to change in future releases.
"""
framework = "pt"
output_dir: str = field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
evaluation_strategy: Union[IntervalStrategy, str] = field(
default="no",
metadata={"help": "The evaluation strategy to use."},
)
prediction_loss_only: bool = field(
default=False,
metadata={"help": "When performing evaluation and predictions, only returns the loss."},
)
NPU_VISIBLE_DEVICES: int = field(
default=None, metadata={"help": "NPU_VISIBLE_DEVICES used for training."}
)
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
per_gpu_train_batch_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Deprecated, the use of `--per_device_train_batch_size` is preferred. "
"Batch size per GPU/TPU core/CPU for training."
)
},
)
per_gpu_eval_batch_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Deprecated, the use of `--per_device_eval_batch_size` is preferred. "
"Batch size per GPU/TPU core/CPU for evaluation."
)
},
)
gradient_accumulation_steps: int = field(
default=1,
metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
)
eval_accumulation_steps: Optional[int] = field(
default=None,
metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."},
)
eval_delay: Optional[float] = field(
default=0,
metadata={
"help": (
"Number of epochs or steps to wait for before the first evaluation can be performed, depending on the"
" evaluation_strategy."
)
},
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
max_grad_norm: float = field(default=1.0, metadata={"help": "Max gradient norm."})
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
max_steps: int = field(
default=-1,
metadata={"help": "If > 0: set total number of training steps to perform. Override num_train_epochs."},
)
lr_scheduler_type: Union[SchedulerType, str] = field(
default="linear",
metadata={"help": "The scheduler type to use."},
)
warmup_ratio: float = field(
default=0.0, metadata={"help": "Linear warmup over warmup_ratio fraction of total steps."}
)
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
log_level: Optional[str] = field(
default="passive",
metadata={
"help": (
"Logger log level to use on the main node. Possible choices are the log levels as strings: 'debug',"
" 'info', 'warning', 'error' and 'critical', plus a 'passive' level which doesn't set anything and"
" lets the application set the level. Defaults to 'passive'."
),
"choices": trainer_log_levels.keys(),
},
)
log_level_replica: Optional[str] = field(
default="warning",
metadata={
"help": "Logger log level to use on replica nodes. Same choices and defaults as ``log_level``",
"choices": trainer_log_levels.keys(),
},
)
log_on_each_node: bool = field(
default=True,
metadata={
"help": (
"When doing a multinode distributed training, whether to log once per node or just once on the main"
" node."
)
},
)
logging_dir: Optional[str] = field(default=None, metadata={"help": "Tensorboard log dir."})
logging_strategy: Union[IntervalStrategy, str] = field(
default="steps",
metadata={"help": "The logging strategy to use."},
)
logging_first_step: bool = field(default=False, metadata={"help": "Log the first global_step"})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
logging_nan_inf_filter: bool = field(default=True, metadata={"help": "Filter nan and inf losses for logging."})
save_strategy: Union[IntervalStrategy, str] = field(
default="steps",
metadata={"help": "The checkpoint save strategy to use."},
)
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
save_total_limit: Optional[int] = field(
default=None,
metadata={
"help": (
"Limit the total amount of checkpoints. "
"Deletes the older checkpoints in the output_dir. Default is unlimited checkpoints"
)
},
)
save_on_each_node: bool = field(
default=False,
metadata={
"help": (
"When doing multi-node distributed training, whether to save models and checkpoints on each node, or"
" only on the main one"
)
},
)
no_cuda: bool = field(default=False, metadata={"help": "Do not use CUDA even when it is available"})
use_mps_device: bool = field(
default=False, metadata={"help": "Whether to use Apple Silicon chip based `mps` device."}
)
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
data_seed: Optional[int] = field(default=None, metadata={"help": "Random seed to be used with data samplers."})
jit_mode_eval: bool = field(
default=False, metadata={"help": "Whether or not to use PyTorch jit trace for inference"}
)
use_ipex: bool = field(
default=False,
metadata={
"help": (
"Use Intel extension for PyTorch when it is available, installation:"
" 'https://github.com/intel/intel-extension-for-pytorch'"
)
},
)
bf16: bool = field(
default=False,
metadata={
"help": (
"Whether to use bf16 (mixed) precision instead of 32-bit. Requires Ampere or higher NVIDIA"
" architecture or using CPU (no_cuda). This is an experimental API and it may change."
)
},
)
fp16: bool = field(
default=False,
metadata={"help": "Whether to use fp16 (mixed) precision instead of 32-bit"},
)
fp16_opt_level: str = field(
default="O1",
metadata={
"help": (
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
"See details at https://nvidia.github.io/apex/amp.html"
)
},
)
half_precision_backend: str = field(
default="auto",
metadata={
"help": "The backend to be used for half precision.",
"choices": ["auto", "cuda_amp", "apex", "cpu_amp"],
},
)
bf16_full_eval: bool = field(
default=False,
metadata={
"help": (
"Whether to use full bfloat16 evaluation instead of 32-bit. This is an experimental API and it may"
" change."
)
},
)
fp16_full_eval: bool = field(
default=False,
metadata={"help": "Whether to use full float16 evaluation instead of 32-bit"},
)
tf32: Optional[bool] = field(
default=None,
metadata={
"help": (
"Whether to enable tf32 mode, available in Ampere and newer GPU architectures. This is an experimental"
" API and it may change."
)
},
)
local_rank: int = field(default=-1, metadata={"help": "For distributed training: local_rank"})
xpu_backend: Optional[str] = field(
default=None,
metadata={
"help": "The backend to be used for distributed training on Intel XPU.",
"choices": ["mpi", "ccl", "gloo"],
},
)
tpu_num_cores: Optional[int] = field(
default=None, metadata={"help": "TPU: Number of TPU cores (automatically passed by launcher script)"}
)
tpu_metrics_debug: bool = field(
default=False,
metadata={
"help": (
"Deprecated, the use of `--debug tpu_metrics_debug` is preferred. TPU: Whether to print debug metrics"
)
},
)
debug: str = field(
default="",
metadata={
"help": (
"Whether or not to enable debug mode. Current options: "
"`underflow_overflow` (Detect underflow and overflow in activations and weights), "
"`tpu_metrics_debug` (print debug metrics on TPU)."
)
},
)
dataloader_drop_last: bool = field(
default=False, metadata={"help": "Drop the last incomplete batch if it is not divisible by the batch size."}
)
eval_steps: Optional[int] = field(default=None, metadata={"help": "Run an evaluation every X steps."})
dataloader_num_workers: int = field(
default=0,
metadata={
"help": (
"Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded"
" in the main process."
)
},
)
past_index: int = field(
default=-1,
metadata={"help": "If >=0, uses the corresponding part of the output as the past state for next step."},
)
run_name: Optional[str] = field(
default=None, metadata={"help": "An optional descriptor for the run. Notably used for wandb logging."}
)
disable_tqdm: Optional[bool] = field(
default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."}
)
remove_unused_columns: Optional[bool] = field(
default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
)
label_names: Optional[List[str]] = field(
default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."}
)
load_best_model_at_end: Optional[bool] = field(
default=False,
metadata={"help": "Whether or not to load the best model found during training at the end of training."},
)
metric_for_best_model: Optional[str] = field(
default=None, metadata={"help": "The metric to use to compare two different models."}
)
greater_is_better: Optional[bool] = field(
default=None, metadata={"help": "Whether the `metric_for_best_model` should be maximized or not."}
)
ignore_data_skip: bool = field(
default=False,
metadata={
"help": (
"When resuming training, whether or not to skip the first epochs and batches to get to the same"
" training data."
)
},
)
sharded_ddp: str = field(
default="",
metadata={
"help": (
"Whether or not to use sharded DDP training (in distributed training only). The base option should be"
" `simple`, `zero_dp_2` or `zero_dp_3` and you can add CPU-offload to `zero_dp_2` or `zero_dp_3` like"
" this: zero_dp_2 offload` or `zero_dp_3 offload`. You can add auto-wrap to `zero_dp_2` or `zero_dp_3`"
" with the same syntax: zero_dp_2 auto_wrap` or `zero_dp_3 auto_wrap`."
),
},
)
fsdp: str = field(
default="",
metadata={
"help": (
"Whether or not to use PyTorch Fully Sharded Data Parallel (FSDP) training (in distributed training"
" only). The base option should be `full_shard`, `shard_grad_op` or `no_shard` and you can add"
" CPU-offload to `full_shard` or `shard_grad_op` like this: full_shard offload` or `shard_grad_op"
" offload`. You can add auto-wrap to `full_shard` or `shard_grad_op` with the same syntax: full_shard"
" auto_wrap` or `shard_grad_op auto_wrap`."
),
},
)
fsdp_min_num_params: int = field(
default=0,
metadata={
"help": (
"This parameter is deprecated. FSDP's minimum number of parameters for Default Auto Wrapping. (useful"
" only when `fsdp` field is passed)."
)
},
)
fsdp_config: Optional[str] = field(
default=None,
metadata={
"help": (
"Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a"
"fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`."
)
},
)
fsdp_transformer_layer_cls_to_wrap: Optional[str] = field(
default=None,
metadata={
"help": (
"This parameter is deprecated. Transformer layer class name (case-sensitive) to wrap, e.g,"
" `BertLayer`, `GPTJBlock`, `T5Block` .... (useful only when `fsdp` flag is passed)."
)
},
)
deepspeed: Optional[str] = field(
default=None,
metadata={
"help": (
"Enable deepspeed and pass the path to deepspeed json config file (e.g. ds_config.json) or an already"
" loaded json file as a dict"
)
},
)
label_smoothing_factor: float = field(
default=0.0, metadata={"help": "The label smoothing epsilon to apply (zero means no label smoothing)."}
)
default_optim = "adamw_hf"
# XXX: enable when pytorch==2.0.1 comes out - we want to give it time to get all the bugs sorted out
# if is_torch_available() and version.parse(version.parse(torch.__version__).base_version) >= version.parse("2.1.0"):
# default_optim = "adamw_torch_fused"
# and update the doc above to:
# optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch_fused"` (for torch<2.1.0 `"adamw_hf"`):
optim: Union[OptimizerNames, str] = field(
default=default_optim,
metadata={"help": "The optimizer to use."},
)
optim_args: Optional[str] = field(default=None, metadata={"help": "Optional arguments to supply to optimizer."})
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
group_by_length: bool = field(
default=False,
metadata={"help": "Whether or not to group samples of roughly the same length together when batching."},
)
length_column_name: Optional[str] = field(
default="length",
metadata={"help": "Column name with precomputed lengths to use when grouping by length."},
)
report_to: Optional[List[str]] = field(
default=None, metadata={"help": "The list of integrations to report the results and logs to."}
)
ddp_find_unused_parameters: Optional[bool] = field(
default=None,
metadata={
"help": (
"When using distributed training, the value of the flag `find_unused_parameters` passed to "
"`DistributedDataParallel`."
)
},
)
ddp_bucket_cap_mb: Optional[int] = field(
default=None,
metadata={
"help": (
"When using distributed training, the value of the flag `bucket_cap_mb` passed to "
"`DistributedDataParallel`."
)
},
)
dataloader_pin_memory: bool = field(
default=True, metadata={"help": "Whether or not to pin memory for DataLoader."}
)
skip_memory_metrics: bool = field(
default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."}
)
use_legacy_prediction_loop: bool = field(
default=False, metadata={"help": "Whether or not to use the legacy prediction_loop in the Trainer."}
)
push_to_hub: bool = field(
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
)
resume_from_checkpoint: Optional[str] = field(
default=None,
metadata={"help": "The path to a folder with a valid checkpoint for your model."},
)
hub_model_id: Optional[str] = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
)
hub_strategy: Union[HubStrategy, str] = field(
default="every_save",
metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."},
)
hub_token: Optional[str] = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
hub_private_repo: bool = field(default=False, metadata={"help": "Whether the model repository is private or not."})
gradient_checkpointing: bool = field(
default=False,
metadata={
"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
},
)
include_inputs_for_metrics: bool = field(
default=False, metadata={"help": "Whether or not the inputs will be passed to the `compute_metrics` function."}
)
# Deprecated arguments
fp16_backend: str = field(
default="auto",
metadata={
"help": "Deprecated. Use half_precision_backend instead",
"choices": ["auto", "cuda_amp", "apex", "cpu_amp"],
},
)
push_to_hub_model_id: Optional[str] = field(
default=None, metadata={"help": "The name of the repository to which push the `Trainer`."}
)
push_to_hub_organization: Optional[str] = field(
default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."}
)
push_to_hub_token: Optional[str] = field(
default=None, metadata={"help": "The token to use to push to the Model Hub."}
)
_n_gpu: int = field(init=False, repr=False, default=-1)
mp_parameters: str = field(
default="",
metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in Trainer"},
)
auto_find_batch_size: bool = field(
default=False,
metadata={
"help": (
"Whether to automatically decrease the batch size in half and rerun the training loop again each time"
" a CUDA Out-of-Memory was reached"
)
},
)
full_determinism: bool = field(
default=False,
metadata={
"help": (
"Whether to call enable_full_determinism instead of set_seed for reproducibility in distributed"
" training"
)
},
)
torchdynamo: Optional[str] = field(
default=None,
metadata={
"help": "This argument is deprecated, use `--torch_compile_backend` instead.",
},
)
ray_scope: Optional[str] = field(
default="last",
metadata={
"help": (
'The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray'
" will then use the last checkpoint of all trials, compare those, and select the best one. However,"
" other options are also available. See the Ray documentation"
" (https://docs.ray.io/en/latest/tune/api_docs/analysis.html"
"#ray.tune.ExperimentAnalysis.get_best_trial)"
" for more options."
)
},
)
ddp_timeout: Optional[int] = field(
default=1800,
metadata={
"help": "Overrides the default timeout for distributed training (value should be given in seconds)."
},
)
torch_compile: bool = field(
default=False, metadata={"help": "If set to `True`, the model will be wrapped in `torch.compile`."}
)
torch_compile_backend: Optional[str] = field(
default=None,
metadata={
"help": "Which backend to use with `torch.compile`, passing one will trigger a model compilation.",
},
)
torch_compile_mode: Optional[str] = field(
default=None,
metadata={
"help": "Which mode to use with `torch.compile`, passing one will trigger a model compilation.",
},
)
def __post_init__(self):
# Handle --use_env option in torch.distributed.launch (local_rank not passed as an arg then).
# This needs to happen before any call to self.device or self.n_gpu.
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != self.local_rank:
self.local_rank = env_local_rank
# expand paths, if not os.makedirs("~/bar") will make directory
# in the current directory instead of the actual home
# see https://github.com/huggingface/transformers/issues/10628
if self.output_dir is not None:
self.output_dir = os.path.expanduser(self.output_dir)
if self.logging_dir is None and self.output_dir is not None:
self.logging_dir = os.path.join(self.output_dir, default_logdir())
if self.logging_dir is not None:
self.logging_dir = os.path.expanduser(self.logging_dir)
if self.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
if isinstance(self.evaluation_strategy, EvaluationStrategy):
warnings.warn(
"using `EvaluationStrategy` for `evaluation_strategy` is deprecated and will be removed in version 5"
" of 馃 Transformers. Use `IntervalStrategy` instead",
FutureWarning,
)
# Go back to the underlying string or we won't be able to instantiate `IntervalStrategy` on it.
self.evaluation_strategy = self.evaluation_strategy.value
self.evaluation_strategy = IntervalStrategy(self.evaluation_strategy)
self.logging_strategy = IntervalStrategy(self.logging_strategy)
self.save_strategy = IntervalStrategy(self.save_strategy)
self.hub_strategy = HubStrategy(self.hub_strategy)
self.lr_scheduler_type = SchedulerType(self.lr_scheduler_type)
if self.do_eval is False and self.evaluation_strategy != IntervalStrategy.NO:
self.do_eval = True
# eval_steps has to be defined and non-zero, fallbacks to logging_steps if the latter is non-zero
if self.evaluation_strategy == IntervalStrategy.STEPS and (self.eval_steps is None or self.eval_steps == 0):
if self.logging_steps > 0:
logger.info(f"using `logging_steps` to initialize `eval_steps` to {self.logging_steps}")
self.eval_steps = self.logging_steps
else:
raise ValueError(
f"evaluation strategy {self.evaluation_strategy} requires either non-zero --eval_steps or"
" --logging_steps"
)
# logging_steps must be non-zero for logging_strategy that is other than 'no'
if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps == 0:
raise ValueError(f"logging strategy {self.logging_strategy} requires non-zero --logging_steps")
# Sanity checks for load_best_model_at_end: we require save and eval strategies to be compatible.
if self.load_best_model_at_end:
if self.evaluation_strategy != self.save_strategy:
raise ValueError(
"--load_best_model_at_end requires the save and eval strategy to match, but found\n- Evaluation "
f"strategy: {self.evaluation_strategy}\n- Save strategy: {self.save_strategy}"
)
if self.evaluation_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
raise ValueError(
"--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation "
f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}."
)
if self.load_best_model_at_end and self.metric_for_best_model is None:
self.metric_for_best_model = "loss"
if self.greater_is_better is None and self.metric_for_best_model is not None:
self.greater_is_better = self.metric_for_best_model not in ["loss", "eval_loss"]
if self.run_name is None:
self.run_name = self.output_dir
if self.framework == "pt" and is_torch_available():
if self.fp16_backend and self.fp16_backend != "auto":
warnings.warn(
"`fp16_backend` is deprecated and will be removed in version 5 of 馃 Transformers. Use"
" `half_precision_backend` instead",
FutureWarning,
)
self.half_precision_backend = self.fp16_backend
if self.bf16 or self.bf16_full_eval:
if self.no_cuda and not is_torch_bf16_cpu_available() and not is_torch_tpu_available():
# cpu
raise ValueError("Your setup doesn't support bf16/(cpu, tpu, neuroncore). You need torch>=1.10")
elif not self.no_cuda and torch.cuda.is_available() and not is_torch_bf16_gpu_available():
# gpu
raise ValueError(
"Your setup doesn't support bf16/gpu. You need torch>=1.10, using Ampere GPU with cuda>=11.0"
)
if self.fp16 and self.bf16:
raise ValueError("At most one of fp16 and bf16 can be True, but not both")
if self.fp16_full_eval and self.bf16_full_eval:
raise ValueError("At most one of fp16 and bf16 can be True for full eval, but not both")
if self.bf16:
if self.half_precision_backend == "apex":
raise ValueError(
" `--half_precision_backend apex`: GPU bf16 is not supported by apex. Use"
" `--half_precision_backend cuda_amp` instead"
)
if not (self.sharded_ddp == "" or not self.sharded_ddp):
raise ValueError("sharded_ddp is not supported with bf16")
self.optim = OptimizerNames(self.optim)
if self.adafactor:
warnings.warn(
"`--adafactor` is deprecated and will be removed in version 5 of 馃 Transformers. Use `--optim"
" adafactor` instead",
FutureWarning,
)
self.optim = OptimizerNames.ADAFACTOR
if self.optim == OptimizerNames.ADAMW_TORCH_FUSED and is_torch_available():
if version.parse(version.parse(torch.__version__).base_version) < version.parse("2.0.0"):
raise ValueError("--optim adamw_torch_fused requires PyTorch 2.0 or higher")
# there is a bug in fp16/AMP in pt-2.0.0
if version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.0") and self.fp16:
raise ValueError("--optim adamw_torch_fused with --fp16 requires PyTorch>2.0")
if (
self.framework == "pt"
and is_torch_available()
and (self.device.type != "npu")
and (get_xla_device_type(self.device) != "NPU")
and (self.fp16 or self.fp16_full_eval)
):
raise ValueError(
"FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation"
" (`--fp16_full_eval`) can only be used on CUDA devices."
)
if (
self.framework == "pt"
and is_torch_available()
and (self.device.type != "npu")
and (get_xla_device_type(self.device) != "NPU")
and (get_xla_device_type(self.device) != "TPU")
and (self.device.type != "cpu")
and (self.bf16 or self.bf16_full_eval)
):
raise ValueError(
"BF16 Mixed precision training with AMP (`--bf16`) and BF16 half precision evaluation"
" (`--bf16_full_eval`) can only be used on CUDA or CPU/TPU/NeuronCore devices."
)
if self.torchdynamo is not None:
warnings.warn(
"`torchdynamo` is deprecated and will be removed in version 5 of 馃 Transformers. Use"
" `torch_compile_backend` instead",
FutureWarning,
)
self.torch_compile_backend = self.torchdynamo
if (self.torch_compile_mode is not None or self.torch_compile_backend is not None) and not self.torch_compile:
self.torch_compile = True
if self.torch_compile and self.torch_compile_backend is None:
self.torch_compile_backend = "inductor"
if self.framework == "pt" and is_torch_available() and self.torch_compile:
if is_torch_tf32_available():
if self.tf32 is None and not self.fp16 or self.bf16:
logger.info(
"Setting TF32 in CUDA backends to speedup torch compile, you won't see any improvement"
" otherwise."
)
torch.backends.cuda.matmul.allow_tf32 = True
else:
logger.warning(
"The speedups for torchdynamo mostly come wih GPU Ampere or higher and which is not detected here."
)
if self.framework == "pt" and is_torch_available() and self.tf32 is not None:
if self.tf32:
if is_torch_tf32_available():
torch.backends.cuda.matmul.allow_tf32 = True
else:
raise ValueError("--tf32 requires Ampere or a newer GPU arch, cuda>=11 and torch>=1.7")
else:
if is_torch_tf32_available():
torch.backends.cuda.matmul.allow_tf32 = False
# no need to assert on else
if self.report_to is None:
logger.info(
"The default value for the training argument `--report_to` will change in v5 (from all installed "
"integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as "
"now. You should start updating your code and make this info disappear :-)."
)
self.report_to = "all"
if self.report_to == "all" or self.report_to == ["all"]:
# Import at runtime to avoid a circular import.
from .integrations import get_available_reporting_integrations
self.report_to = get_available_reporting_integrations()
elif self.report_to == "none" or self.report_to == ["none"]:
self.report_to = []
elif not isinstance(self.report_to, list):
self.report_to = [self.report_to]
if self.warmup_ratio < 0 or self.warmup_ratio > 1:
raise ValueError("warmup_ratio must lie in range [0,1]")
elif self.warmup_ratio > 0 and self.warmup_steps > 0:
logger.info(
"Both warmup_ratio and warmup_steps given, warmup_steps will override any effect of warmup_ratio"
" during training"
)
if isinstance(self.sharded_ddp, bool):
self.sharded_ddp = "simple" if self.sharded_ddp else ""
if isinstance(self.sharded_ddp, str):
self.sharded_ddp = [ShardedDDPOption(s) for s in self.sharded_ddp.split()]
if self.sharded_ddp == [ShardedDDPOption.OFFLOAD]:
raise ValueError(
"`--sharded_ddp offload` can't work on its own. It needs to be added to `--sharded_ddp zero_dp_2` or "
'`--sharded_ddp zero_dp_3`. For example, `--sharded_ddp "zero_dp_2 offload"`.'
)
elif len(self.sharded_ddp) > 1 and ShardedDDPOption.SIMPLE in self.sharded_ddp:
raise ValueError("`--sharded_ddp simple` is not compatible with any other option.")
elif ShardedDDPOption.ZERO_DP_2 in self.sharded_ddp and ShardedDDPOption.ZERO_DP_3 in self.sharded_ddp:
raise ValueError("`--sharded_ddp zero_dp_2` is not compatible with `--sharded_ddp zero_dp_3`.")
if isinstance(self.fsdp, bool):
self.fsdp = "full_shard" if self.fsdp else ""
if isinstance(self.fsdp, str):
self.fsdp = [FSDPOption(s) for s in self.fsdp.split()]
if self.fsdp == [FSDPOption.OFFLOAD]:
raise ValueError(
"`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or "
'`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.'
)
elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp:
raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.")
if self.fsdp_config is None:
self.fsdp_config = {}
if isinstance(self.fsdp_config, str):
with io.open(self.fsdp_config, "r", encoding="utf-8") as f:
self.fsdp_config = json.load(f)
if self.fsdp_min_num_params > 0:
warnings.warn("using `--fsdp_min_num_params` is deprecated. Use fsdp_config instead ", FutureWarning)
self.fsdp_config["fsdp_min_num_params"] = max(
self.fsdp_config.get("fsdp_min_num_params", 0), self.fsdp_min_num_params
)
# if fsdp_config["fsdp_transformer_layer_cls_to_wrap"] is specified as a string, convert it to a list with a single object
if isinstance(self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None), str):
self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = [
self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"]
]
if self.fsdp_transformer_layer_cls_to_wrap is not None:
warnings.warn(
"using `--fsdp_transformer_layer_cls_to_wrap` is deprecated. Use fsdp_config instead ", FutureWarning
)
self.fsdp_config["fsdp_transformer_layer_cls_to_wrap"] = self.fsdp_config.get(
"fsdp_transformer_layer_cls_to_wrap", []
) + [self.fsdp_transformer_layer_cls_to_wrap]
if len(self.fsdp) == 0 and self.fsdp_config["fsdp_min_num_params"] > 0:
warnings.warn("`--fsdp_min_num_params` is useful only when `--fsdp` is specified.")
if len(self.fsdp) == 0 and self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None:
warnings.warn("`--fsdp_transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")
if (
len(self.fsdp) > 0
and self.fsdp_config["fsdp_min_num_params"] > 0
and self.fsdp_config.get("fsdp_transformer_layer_cls_to_wrap", None) is not None
):
raise ValueError(
"`--fsdp_min_num_params` and `--fsdp_transformer_layer_cls_to_wrap` are mutually exclusive."
)
self.fsdp_config["xla"] = self.fsdp_config.get("xla", False)
self.fsdp_config["xla_fsdp_grad_ckpt"] = self.fsdp_config.get("xla_fsdp_grad_ckpt", False)
if self.fsdp_config["xla"]:
if len(self.fsdp) > 0:
# store XLA fsdp configuration parameters into a dictionary
self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {})
# apply appropriate string to torch.dtype conversions for parameters
if "compute_dtype" in self.xla_fsdp_config:
self.xla_fsdp_config["compute_dtype"] = getattr(torch, self.xla_fsdp_config["compute_dtype"])
if "buffer_dtype" in self.xla_fsdp_config:
self.xla_fsdp_config["buffer_dtype"] = getattr(torch, self.xla_fsdp_config["buffer_dtype"])
else:
warnings.warn("XLA FSDP can be used only when `--fsdp` is specified.")
else:
if self.fsdp_config["xla_fsdp_grad_ckpt"]:
warnings.warn("`--xla_fsdp_grad_ckpt` is useful only when `--xla` is set to true.")
if self.tpu_metrics_debug:
warnings.warn(
"using `--tpu_metrics_debug` is deprecated and will be removed in version 5 of 馃 Transformers. Use"
" `--debug tpu_metrics_debug` instead",
FutureWarning,
)
self.debug += " tpu_metrics_debug"
self.tpu_metrics_debug = False
if isinstance(self.debug, str):
self.debug = [DebugOption(s) for s in self.debug.split()]
if self.deepspeed:
# - must be run very last in arg parsing, since it will use a lot of these settings.
# - must be run before the model is created.
if not is_accelerate_available():
raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.")
from transformers.deepspeed import HfTrainerDeepSpeedConfig
# will be used later by the Trainer
# note: leave self.deepspeed unmodified in case a user relies on it not to be modified)
self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed)
self.hf_deepspeed_config.trainer_config_process(self)
if self.push_to_hub_token is not None:
warnings.warn(
"`--push_to_hub_token` is deprecated and will be removed in version 5 of 馃 Transformers. Use "
"`--hub_token` instead.",
FutureWarning,
)
self.hub_token = self.push_to_hub_token
if self.push_to_hub_model_id is not None:
self.hub_model_id = get_full_repo_name(
self.push_to_hub_model_id, organization=self.push_to_hub_organization, token=self.hub_token
)
if self.push_to_hub_organization is not None:
warnings.warn(
"`--push_to_hub_model_id` and `--push_to_hub_organization` are deprecated and will be removed in "
"version 5 of 馃 Transformers. Use `--hub_model_id` instead and pass the full repo name to this "
f"argument (in this case {self.hub_model_id}).",
FutureWarning,
)
else:
warnings.warn(
"`--push_to_hub_model_id` is deprecated and will be removed in version 5 of 馃 Transformers. Use "
"`--hub_model_id` instead and pass the full repo name to this argument (in this case "
f"{self.hub_model_id}).",
FutureWarning,
)
elif self.push_to_hub_organization is not None:
self.hub_model_id = f"{self.push_to_hub_organization}/{Path(self.output_dir).name}"
warnings.warn(
"`--push_to_hub_organization` is deprecated and will be removed in version 5 of 馃 Transformers. Use "
"`--hub_model_id` instead and pass the full repo name to this argument (in this case "
f"{self.hub_model_id}).",
FutureWarning,
)
def __str__(self):
self_as_dict = asdict(self)
# Remove deprecated arguments. That code should be removed once
# those deprecated arguments are removed from TrainingArguments. (TODO: v5)
del self_as_dict["per_gpu_train_batch_size"]
del self_as_dict["per_gpu_eval_batch_size"]
self_as_dict = {k: f"<{k.upper()}>" if k.endswith("_token") else v for k, v in self_as_dict.items()}
attrs_as_str = [f"{k}={v},\n" for k, v in sorted(self_as_dict.items())]
return f"{self.__class__.__name__}(\n{''.join(attrs_as_str)})"
__repr__ = __str__
@property
def train_batch_size(self) -> int:
"""
The actual batch size for training (may differ from `per_gpu_train_batch_size` in distributed training).
"""
if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
"version. Using `--per_device_train_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
train_batch_size = per_device_batch_size * max(1, self.n_gpu)
return train_batch_size
@property
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation (may differ from `per_gpu_eval_batch_size` in distributed training).
"""
if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
"version. Using `--per_device_eval_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
eval_batch_size = per_device_batch_size * max(1, self.n_gpu)
return eval_batch_size
@property
def ddp_timeout_delta(self) -> timedelta:
"""
The actual timeout for torch.distributed.init_process_group since it expects a timedelta variable.
"""
return timedelta(seconds=self.ddp_timeout)
@cached_property
def _setup_devices(self) -> "torch.device":
requires_backends(self, ["torch"])
logger.info("PyTorch: setting up devices")
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"torch.distributed process group is initialized, but local_rank == -1. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
)
if self.no_cuda:
device = torch.device("cpu")
self._n_gpu = 0
self.local_rank = get_int_from_env(
["LOCAL_RANK", "MPI_LOCALRANKID", "OMPI_COMM_WORLD_LOCAL_RANK", "MV2_COMM_WORLD_LOCAL_RANK"],
self.local_rank,
)
if self.local_rank != -1 and not torch.distributed.is_initialized():
# Initializes distributed backend for cpu
if self.xpu_backend not in ("mpi", "ccl", "gloo"):
raise ValueError(
"CPU distributed training backend is not properly set. "
"Please set '--xpu_backend' to either 'mpi' or 'ccl' or 'gloo'."
)
if self.xpu_backend == "ccl":
requires_backends(self, "oneccl_bind_pt")
if ccl_version >= "1.12":
import oneccl_bindings_for_pytorch # noqa: F401
else:
import torch_ccl # noqa: F401
if int(os.environ.get("CCL_WORKER_COUNT", 0)) < 1:
raise ValueError(
"CPU distributed training backend is ccl. but CCL_WORKER_COUNT is not correctly set. "
"Please use like 'export CCL_WORKER_COUNT = 1' to set."
)
# Try to get launch configuration from environment variables set by MPI launcher - works for Intel MPI, OpenMPI and MVAPICH
rank = get_int_from_env(["RANK", "PMI_RANK", "OMPI_COMM_WORLD_RANK", "MV2_COMM_WORLD_RANK"], 0)
size = get_int_from_env(["WORLD_SIZE", "PMI_SIZE", "OMPI_COMM_WORLD_SIZE", "MV2_COMM_WORLD_SIZE"], 1)
local_size = get_int_from_env(
["MPI_LOCALNRANKS", "OMPI_COMM_WORLD_LOCAL_SIZE", "MV2_COMM_WORLD_LOCAL_SIZE"], 1
)
os.environ["RANK"] = str(rank)
os.environ["WORLD_SIZE"] = str(size)
os.environ["LOCAL_RANK"] = str(self.local_rank)
if not os.environ.get("MASTER_PORT", None):
os.environ["MASTER_PORT"] = "29500"
if not os.environ.get("MASTER_ADDR", None):
if local_size != size or self.xpu_backend != "mpi":
raise ValueError(
"Looks like distributed multinode run but MASTER_ADDR env not set, "
"please try exporting rank 0's hostname as MASTER_ADDR"
)
if (
torch.get_num_threads() == 1
and get_int_from_env(["OMP_NUM_THREADS", "MKL_NUM_THREADS"], 0) == 0
and is_psutil_available()
):
import psutil
num_cpu_threads_per_process = int(psutil.cpu_count(logical=False) / local_size)
if num_cpu_threads_per_process == 0:
num_cpu_threads_per_process = 1
torch.set_num_threads(num_cpu_threads_per_process)
logger.info(
f"num_cpu_threads_per_process unset, we set it at {num_cpu_threads_per_process} to improve oob"
" performance."
)
torch.distributed.init_process_group(
backend=self.xpu_backend, rank=rank, world_size=size, timeout=self.ddp_timeout_delta
)
elif is_torch_tpu_available():
device = xm.xla_device()
self._n_gpu = 0
elif is_sagemaker_mp_enabled():
local_rank = smp.local_rank()
device = torch.device("npu", local_rank)
self._n_gpu = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
dist.init_process_group(backend="smddp", timeout=self.ddp_timeout_delta)
self.local_rank = int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"))
device = torch.device("npu", self.local_rank)
self._n_gpu = 1
elif self.deepspeed:
# deepspeed inits torch.distributed internally
from .deepspeed import is_deepspeed_available
if not is_deepspeed_available():
raise ImportError("--deepspeed requires deepspeed: `pip install deepspeed`.")
import deepspeed
deepspeed.init_distributed(timeout=timedelta(seconds=self.ddp_timeout))
# workaround for setups like notebooks where the launcher can't be used,
# but deepspeed requires a dist env.
# env LOCAL_RANK could be set manually by the user, or via init_distributed if mpi4py is installed
self.local_rank = int(os.environ.get("LOCAL_RANK", "-1"))
device = torch.device("npu", self.local_rank)
self._n_gpu = 1
elif self.local_rank == -1:
if self.use_mps_device:
if not torch.backends.mps.is_available():
if not torch.backends.mps.is_built():
raise AssertionError(
"MPS not available because the current PyTorch install was not "
"built with MPS enabled. Please install torch version >=1.12.0 on "
"your Apple silicon Mac running macOS 12.3 or later with a native "
"version (arm64) of Python"
)
else:
raise AssertionError(
"MPS not available because the current MacOS version is not 12.3+ "
"and/or you do not have an MPS-enabled device on this machine."
)
else:
if not version.parse(version.parse(torch.__version__).base_version) > version.parse("1.12.0"):
warnings.warn(
"We strongly recommend to install PyTorch >= 1.13 (nightly version at the time of writing)"
" on your MacOS machine. It has major fixes related to model correctness and performance"
" improvements for transformer based models. Please refer to"
" https://github.com/pytorch/pytorch/issues/82707 for more details."
)
device = torch.device("mps")
self._n_gpu = 1
else:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
if self.NPU_VISIBLE_DEVICES:
device = torch.device(f"npu:{self.NPU_VISIBLE_DEVICES}" if torch.npu.is_available() else "cpu")
self._n_gpu = 1
else:
device = torch.device("npu:0" if torch.npu.is_available() else "cpu")
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
self._n_gpu = torch.npu.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="nccl", timeout=self.ddp_timeout_delta)
device = torch.device("npu", self.local_rank)
self._n_gpu = 1
if device.type == "npu":
torch.cuda.set_device(device)
return device
@property
def device(self) -> "torch.device":
"""
The device used by this process.
"""
requires_backends(self, ["torch"])
return self._setup_devices
@property
def n_gpu(self):
"""
The number of GPUs used by this process.
Note:
This will only be greater than one when you have multiple GPUs available but are not using distributed
training. For distributed training, it will always be 1.
"""
requires_backends(self, ["torch"])
# Make sure `self._n_gpu` is properly setup.
_ = self._setup_devices
return self._n_gpu
@property
def parallel_mode(self):
"""
The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:
- `ParallelMode.NOT_PARALLEL`: no parallelism (CPU or one GPU).
- `ParallelMode.NOT_DISTRIBUTED`: several GPUs in one single process (uses `torch.nn.DataParallel`).
- `ParallelMode.DISTRIBUTED`: several GPUs, each having its own process (uses
`torch.nn.DistributedDataParallel`).
- `ParallelMode.TPU`: several TPU cores.
"""
requires_backends(self, ["torch"])
if is_torch_tpu_available():
return ParallelMode.TPU
elif is_sagemaker_mp_enabled():
return ParallelMode.SAGEMAKER_MODEL_PARALLEL
elif is_sagemaker_dp_enabled():
return ParallelMode.SAGEMAKER_DATA_PARALLEL
elif self.local_rank != -1:
return ParallelMode.DISTRIBUTED
elif self.n_gpu > 1:
return ParallelMode.NOT_DISTRIBUTED
else:
return ParallelMode.NOT_PARALLEL
@property
def world_size(self):
"""
The number of processes used in parallel.
"""
requires_backends(self, ["torch"])
if is_torch_tpu_available():
return xm.xrt_world_size()
elif is_sagemaker_mp_enabled():
return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size()
elif is_sagemaker_dp_enabled():
return dist.get_world_size()
elif self.local_rank != -1:
return torch.distributed.get_world_size()
return 1
@property
def process_index(self):
"""
The index of the current process used.
"""
requires_backends(self, ["torch"])
if is_torch_tpu_available():
return xm.get_ordinal()
elif is_sagemaker_mp_enabled():
return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank()
elif is_sagemaker_dp_enabled():
return dist.get_rank()
elif self.local_rank != -1:
return torch.distributed.get_rank()
return 0
@property
def local_process_index(self):
"""
The index of the local process used.
"""
requires_backends(self, ["torch"])
if is_torch_tpu_available():
return xm.get_local_ordinal()
elif is_sagemaker_mp_enabled():
return smp.local_rank()
elif is_sagemaker_dp_enabled():
return dist.get_rank()
elif self.local_rank != -1:
return self.local_rank
return 0
@property
def should_log(self):
"""
Whether or not the current process should produce log.
"""
if self.log_on_each_node:
return self.local_process_index == 0
else:
if is_sagemaker_mp_enabled():
return smp.rank() == 0
else:
return self.process_index == 0
@property
def should_save(self):
"""
Whether or not the current process should write to disk, e.g., to save models and checkpoints.
"""
if self.save_on_each_node:
return self.local_process_index == 0
else:
if is_sagemaker_mp_enabled():
return smp.rank() == 0
else:
return self.process_index == 0
def get_process_log_level(self):
"""
Returns the log level to be used depending on whether this process is the main process of node 0, main process
of node non-0, or a non-main process.
For the main process the log level defaults to the logging level set (`logging.WARNING` if you didn't do
anything) unless overridden by `log_level` argument.
For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica`
argument.
The choice between the main and replica process settings is made according to the return value of `should_log`.
"""
# convert to int
log_level = trainer_log_levels[self.log_level]
log_level_replica = trainer_log_levels[self.log_level_replica]
log_level_main_node = logging.get_verbosity() if log_level == -1 else log_level
log_level_replica_node = logging.get_verbosity() if log_level_replica == -1 else log_level_replica
return log_level_main_node if self.should_log else log_level_replica_node
@property
def place_model_on_device(self):
"""
Can be subclassed and overridden for some specific integrations.
"""
return not is_sagemaker_mp_enabled()
@property
def _no_sync_in_gradient_accumulation(self):
"""
Whether or not to use no_sync for the gradients when doing gradient accumulation.
"""
return not (
self.deepspeed or is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled() or is_torch_neuroncore_available()
)
@contextlib.contextmanager
def main_process_first(self, local=True, desc="work"):
"""
A context manager for torch distributed environment where on needs to do something on the main process, while
blocking replicas, and when it's finished releasing the replicas.
One such use is for `datasets`'s `map` feature which to be efficient should be run once on the main process,
which upon completion saves a cached version of results and which then automatically gets loaded by the
replicas.
Args:
local (`bool`, *optional*, defaults to `True`):
if `True` first means process of rank 0 of each node if `False` first means process of rank 0 of node
rank 0 In multi-node environment with a shared filesystem you most likely will want to use
`local=False` so that only the main process of the first node will do the processing. If however, the
filesystem is not shared, then the main process of each node will need to do the processing, which is
the default behavior.
desc (`str`, *optional*, defaults to `"work"`):
a work description to be used in debug logs
"""
if is_torch_available() and self.world_size > 1:
main_process_desc = "main process"
if local:
is_main_process = self.local_process_index == 0
main_process_desc = "main local process"
elif is_sagemaker_mp_enabled():
is_main_process = smp.rank() == 0
else:
is_main_process = self.process_index == 0
try:
if not is_main_process:
# tell all replicas to wait
logger.debug(f"{self.process_index}: waiting for the {main_process_desc} to perform {desc}")
if is_torch_tpu_available():
xm.rendezvous(desc)
elif is_sagemaker_dp_enabled():
dist.barrier()
else:
torch.distributed.barrier()
yield
finally:
if is_main_process:
# the wait is over
logger.debug(f"{self.process_index}: {main_process_desc} completed {desc}, releasing all replicas")
if is_torch_tpu_available():
xm.rendezvous(desc)
elif is_sagemaker_dp_enabled():
dist.barrier()
else:
torch.distributed.barrier()
else:
yield
def get_warmup_steps(self, num_training_steps: int):
"""
Get number of steps used for a linear warmup.
"""
warmup_steps = (
self.warmup_steps if self.warmup_steps > 0 else math.ceil(num_training_steps * self.warmup_ratio)
)
return warmup_steps
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.
"""
# filter out fields that are defined as field(init=False)
d = {field.name: getattr(self, field.name) for field in fields(self) if field.init}
for k, v in d.items():
if isinstance(v, Enum):
d[k] = v.value
if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
d[k] = [x.value for x in v]
if k.endswith("_token"):
d[k] = f"<{k.upper()}>"
return d
def to_json_string(self):
"""
Serializes this instance to a JSON string.
"""
return json.dumps(self.to_dict(), indent=2)
def to_sanitized_dict(self) -> Dict[str, Any]:
"""
Sanitized serialization to use with TensorBoard鈥檚 hparams
"""
d = self.to_dict()
d = {**d, **{"train_batch_size": self.train_batch_size, "eval_batch_size": self.eval_batch_size}}
valid_types = [bool, int, float, str]
if is_torch_available():
valid_types.append(torch.Tensor)
return {k: v if type(v) in valid_types else str(v) for k, v in d.items()}
# The following methods are there to simplify the instantiation of `TrainingArguments`
def set_training(
self,
learning_rate: float = 5e-5,
batch_size: int = 8,
weight_decay: float = 0,
num_epochs: float = 3,
max_steps: int = -1,
gradient_accumulation_steps: int = 1,
seed: int = 42,
gradient_checkpointing: bool = False,
):
"""
A method that regroups all basic arguments linked to the training.
<Tip>
Calling this method will automatically set `self.do_train` to `True`.
</Tip>
Args:
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for the optimizer.
batch_size (`int` *optional*, defaults to 8):
The batch size per device (GPU/TPU core/CPU...) used for training.
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in the
optimizer.
num_train_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform (if not an integer, will perform the decimal part percents
of the last epoch before stopping training).
max_steps (`int`, *optional*, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides
`num_train_epochs`. In case of using a finite iterable dataset the training may stop before reaching
the set number of steps when all data is exhausted.
gradient_accumulation_steps (`int`, *optional*, defaults to 1):
Number of updates steps to accumulate the gradients for, before performing a backward/update pass.
<Tip warning={true}>
When using gradient accumulation, one step is counted as one step with backward pass. Therefore,
logging, evaluation, save will be conducted every `gradient_accumulation_steps * xxx_step` training
examples.
</Tip>
seed (`int`, *optional*, defaults to 42):
Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use
the [`~Trainer.model_init`] function to instantiate the model if it has some randomly initialized
parameters.
gradient_checkpointing (`bool`, *optional*, defaults to `False`):
If True, use gradient checkpointing to save memory at the expense of slower backward pass.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_training(learning_rate=1e-4, batch_size=32)
>>> args.learning_rate
1e-4
```
"""
self.do_train = True
self.learning_rate = learning_rate
self.per_device_train_batch_size = batch_size
self.weight_decay = weight_decay
self.num_train_epochs = num_epochs
self.max_steps = max_steps
self.gradient_accumulation_steps = gradient_accumulation_steps
self.seed = seed
self.gradient_checkpointing = gradient_checkpointing
return self
def set_evaluate(
self,
strategy: Union[str, IntervalStrategy] = "no",
steps: int = 500,
batch_size: int = 8,
accumulation_steps: Optional[int] = None,
delay: Optional[float] = None,
loss_only: bool = False,
jit_mode: bool = False,
):
"""
A method that regroups all arguments linked to the evaluation.
Args:
strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
The evaluation strategy to adopt during training. Possible values are:
- `"no"`: No evaluation is done during training.
- `"steps"`: Evaluation is done (and logged) every `steps`.
- `"epoch"`: Evaluation is done at the end of each epoch.
Setting a `strategy` different from `"no"` will set `self.do_eval` to `True`.
steps (`int`, *optional*, defaults to 500):
Number of update steps between two evaluations if `strategy="steps"`.
batch_size (`int` *optional*, defaults to 8):
The batch size per device (GPU/TPU core/CPU...) used for evaluation.
accumulation_steps (`int`, *optional*):
Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU.
If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster
but requires more memory).
delay (`float`, *optional*):
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
evaluation_strategy.
loss_only (`bool`, *optional*, defaults to `False`):
Ignores all outputs except the loss.
jit_mode (`bool`, *optional*):
Whether or not to use PyTorch jit trace for inference.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_evaluate(strategy="steps", steps=100)
>>> args.eval_steps
100
```
"""
self.evaluation_strategy = IntervalStrategy(strategy)
if self.evaluation_strategy == IntervalStrategy.STEPS and steps == 0:
raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.")
self.do_eval = self.evaluation_strategy != IntervalStrategy.NO
self.eval_steps = steps
self.per_device_eval_batch_size = batch_size
self.eval_accumulation_steps = accumulation_steps
self.eval_delay = delay
self.prediction_loss_only = loss_only
self.jit_mode_eval = jit_mode
return self
def set_testing(
self,
batch_size: int = 8,
loss_only: bool = False,
jit_mode: bool = False,
):
"""
A method that regroups all basic arguments linked to testing on a held-out dataset.
<Tip>
Calling this method will automatically set `self.do_predict` to `True`.
</Tip>
Args:
batch_size (`int` *optional*, defaults to 8):
The batch size per device (GPU/TPU core/CPU...) used for testing.
loss_only (`bool`, *optional*, defaults to `False`):
Ignores all outputs except the loss.
jit_mode (`bool`, *optional*):
Whether or not to use PyTorch jit trace for inference.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_testing(batch_size=32)
>>> args.per_device_eval_batch_size
32
```
"""
self.do_predict = True
self.per_device_eval_batch_size = batch_size
self.prediction_loss_only = loss_only
self.jit_mode_eval = jit_mode
return self
def set_save(
self,
strategy: Union[str, IntervalStrategy] = "steps",
steps: int = 500,
total_limit: Optional[int] = None,
on_each_node: bool = False,
):
"""
A method that regroups all arguments linked to the evaluation.
Args:
strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The checkpoint save strategy to adopt during training. Possible values are:
- `"no"`: No save is done during training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`.
steps (`int`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `strategy="steps"`.
total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
`output_dir`.
on_each_node (`bool`, *optional*, defaults to `False`):
When doing multi-node distributed training, whether to save models and checkpoints on each node, or
only on the main one.
This should not be activated when the different nodes use the same storage as the files will be saved
with the same names for each node.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_save(strategy="steps", steps=100)
>>> args.save_steps
100
```
"""
self.save_strategy = IntervalStrategy(strategy)
if self.save_strategy == IntervalStrategy.STEPS and steps == 0:
raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.")
self.save_steps = steps
self.save_total_limit = total_limit
self.save_on_each_node = on_each_node
return self
def set_logging(
self,
strategy: Union[str, IntervalStrategy] = "steps",
steps: int = 500,
report_to: Union[str, List[str]] = "none",
level: str = "passive",
first_step: bool = False,
nan_inf_filter: bool = False,
on_each_node: bool = False,
replica_level: str = "passive",
):
"""
A method that regroups all arguments linked to the evaluation.
Args:
strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"steps"`):
The logging strategy to adopt during training. Possible values are:
- `"no"`: No save is done during training.
- `"epoch"`: Save is done at the end of each epoch.
- `"steps"`: Save is done every `save_steps`.
steps (`int`, *optional*, defaults to 500):
Number of update steps between two logs if `strategy="steps"`.
level (`str`, *optional*, defaults to `"passive"`):
Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`,
`"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn't set anything
and lets the application set the level.
report_to (`str` or `List[str]`, *optional*, defaults to `"none"`):
The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,
`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. Use `"all"` to report
to all integrations installed, `"none"` for no integrations.
first_step (`bool`, *optional*, defaults to `False`):
Whether to log and evaluate the first `global_step` or not.
nan_inf_filter (`bool`, *optional*, defaults to `True`):
Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is
`nan` or `inf` is filtered and the average loss of the current logging window is taken instead.
<Tip>
`nan_inf_filter` only influences the logging of loss values, it does not change the behavior the
gradient is computed or applied to the model.
</Tip>
on_each_node (`bool`, *optional*, defaults to `True`):
In multinode distributed training, whether to log using `log_level` once per node, or only on the main
node.
replica_level (`str`, *optional*, defaults to `"passive"`):
Logger log level to use on replicas. Same choices as `log_level`
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_logging(strategy="steps", steps=100)
>>> args.logging_steps
100
```
"""
self.logging_strategy = IntervalStrategy(strategy)
if self.logging_strategy == IntervalStrategy.STEPS and steps == 0:
raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.")
self.logging_steps = steps
self.report_to = report_to
self.log_level = level
self.logging_first_step = first_step
self.logging_nan_inf_filter = nan_inf_filter
self.log_on_each_node = on_each_node
self.log_level_replica = replica_level
return self
def set_push_to_hub(
self,
model_id: str,
strategy: Union[str, HubStrategy] = "every_save",
token: Optional[str] = None,
private_repo: bool = False,
):
"""
A method that regroups all arguments linked to synchronizing checkpoints with the Hub.
<Tip>
Calling this method will set `self.push_to_hub` to `True`, which means the `output_dir` will begin a git
directory synced with the repo (determined by `model_id`) and the content will be pushed each time a save is
triggered (depending on`self.save_strategy`). Calling [`~Trainer.save_model`] will also trigger a push.
</Tip>
Args:
model_id (`str`):
The name of the repository to keep in sync with the local *output_dir*. It can be a simple model ID in
which case the model will be pushed in your namespace. Otherwise it should be the whole repository
name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of
with `"organization_name/model"`.
strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`):
Defines the scope of what is pushed to the Hub and when. Possible values are:
- `"end"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`]) and a
draft of a model card when the [`~Trainer.save_model`] method is called.
- `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the [`Trainer`])
and
a draft of a model card each time there is a model save. The pushes are asynchronous to not block
training, and in case the save are very frequent, a new push is only attempted if the previous one is
finished. A last push is made with the final model at the end of training.
- `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named
last-checkpoint, allowing you to resume training easily with
`trainer.train(resume_from_checkpoint="last-checkpoint")`.
- `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the
output
folder (so you will get one checkpoint folder per folder in your final repository)
token (`str`, *optional*):
The token to use to push the model to the Hub. Will default to the token in the cache folder obtained
with `huggingface-cli login`.
private_repo (`bool`, *optional*, defaults to `False`):
If True, the Hub repo will be set to private.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_push_to_hub("me/awesome-model")
>>> args.hub_model_id
'me/awesome-model'
```
"""
self.push_to_hub = True
self.hub_model_id = model_id
self.hub_strategy = HubStrategy(strategy)
self.hub_token = token
self.hub_private_repo = private_repo
return self
def set_optimizer(
self,
name: Union[str, OptimizerNames] = "adamw_hf",
learning_rate: float = 5e-5,
weight_decay: float = 0,
beta1: float = 0.9,
beta2: float = 0.999,
epsilon: float = 1e-8,
args: Optional[str] = None,
):
"""
A method that regroups all arguments linked to the optimizer and its hyperparameters.
Args:
name (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_hf"`):
The optimizer to use: `"adamw_hf"`, `"adamw_torch"`, `"adamw_torch_fused"`, `"adamw_apex_fused"`,
`"adamw_anyprecision"` or `"adafactor"`.
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate.
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights.
beta1 (`float`, *optional*, defaults to 0.9):
The beta1 hyperparameter for the adam optimizer or its variants.
beta2 (`float`, *optional*, defaults to 0.999):
The beta2 hyperparameter for the adam optimizer or its variants.
epsilon (`float`, *optional*, defaults to 1e-8):
The epsilon hyperparameter for the adam optimizer or its variants.
args (`str`, *optional*):
Optional arguments that are supplied to AnyPrecisionAdamW (only useful when
`optim="adamw_anyprecision"`).
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_optimizer(name="adamw_torch", beta1=0.8)
>>> args.optim
'adamw_torch'
```
"""
self.optim = OptimizerNames(name)
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.adam_beta1 = beta1
self.adam_beta2 = beta2
self.adam_epsilon = epsilon
self.optim_args = args
return self
def set_lr_scheduler(
self,
name: Union[str, SchedulerType] = "linear",
num_epochs: float = 3.0,
max_steps: int = -1,
warmup_ratio: float = 0,
warmup_steps: int = 0,
):
"""
A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters.
Args:
name (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
The scheduler type to use. See the documentation of [`SchedulerType`] for all possible values.
num_epochs(`float`, *optional*, defaults to 3.0):
Total number of training epochs to perform (if not an integer, will perform the decimal part percents
of the last epoch before stopping training).
max_steps (`int`, *optional*, defaults to -1):
If set to a positive number, the total number of training steps to perform. Overrides
`num_train_epochs`. In case of using a finite iterable dataset the training may stop before reaching
the set number of steps when all data is exhausted.
warmup_ratio (`float`, *optional*, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
warmup_steps (`int`, *optional*, defaults to 0):
Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of
`warmup_ratio`.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_lr_scheduler(name="cosine", warmup_ratio=0.05)
>>> args.warmup_ratio
0.05
```
"""
self.lr_scheduler_type = SchedulerType(name)
self.num_train_epochs = num_epochs
self.max_steps = max_steps
self.warmup_ratio = warmup_ratio
self.warmup_steps = warmup_steps
return self
def set_dataloader(
self,
train_batch_size: int = 8,
eval_batch_size: int = 8,
drop_last: bool = False,
num_workers: int = 0,
pin_memory: bool = True,
auto_find_batch_size: bool = False,
ignore_data_skip: bool = False,
sampler_seed: Optional[int] = None,
):
"""
A method that regroups all arguments linked to the dataloaders creation.
Args:
drop_last (`bool`, *optional*, defaults to `False`):
Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch
size) or not.
num_workers (`int`, *optional*, defaults to 0):
Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in
the main process.
pin_memory (`bool`, *optional*, defaults to `True`):
Whether you want to pin memory in data loaders or not. Will default to `True`.
auto_find_batch_size (`bool`, *optional*, defaults to `False`)
Whether to find a batch size that will fit into memory automatically through exponential decay,
avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
ignore_data_skip (`bool`, *optional*, defaults to `False`):
When resuming training, whether or not to skip the epochs and batches to get the data loading at the
same stage as in the previous training. If set to `True`, the training will begin faster (as that
skipping step can take a long time) but will not yield the same results as the interrupted training
would have.
sampler_seed (`int`, *optional*):
Random seed to be used with data samplers. If not set, random generators for data sampling will use the
same seed as `self.seed`. This can be used to ensure reproducibility of data sampling, independent of
the model seed.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64)
>>> args.per_device_train_batch_size
16
```
"""
self.per_device_train_batch_size = train_batch_size
self.per_device_eval_batch_size = eval_batch_size
self.dataloader_drop_last = drop_last
self.dataloader_num_workers = num_workers
self.dataloader_pin_memory = pin_memory
self.auto_find_batch_size = auto_find_batch_size
self.ignore_data_skip = ignore_data_skip
self.data_seed = sampler_seed
return self
class ParallelMode(Enum):
NOT_PARALLEL = "not_parallel"
NOT_DISTRIBUTED = "not_distributed"
DISTRIBUTED = "distributed"
SAGEMAKER_MODEL_PARALLEL = "sagemaker_model_parallel"
SAGEMAKER_DATA_PARALLEL = "sagemaker_data_parallel"
TPU = "tpu"
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