Armintawebapp
/
webapp
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'main'
${ noResults }
webapp/seed/Lib/site-packages/transformers/training_args.py

2804 lines
139 KiB
Raw Permalink Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import json
import math
import os
import warnings
from dataclasses import asdict, dataclass, field, fields
from datetime import timedelta
from enum import Enum
from functools import cached_property
from typing import Any
from .debug_utils import DebugOption
from .trainer_utils import (
FSDPOption,
HubStrategy,
IntervalStrategy,
SaveStrategy,
SchedulerType,
)
from .utils import (
ACCELERATE_MIN_VERSION,
ExplicitEnum,
is_accelerate_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_torch_available,
is_torch_bf16_gpu_available,
is_torch_cuda_available,
is_torch_hpu_available,
is_torch_mlu_available,
is_torch_mps_available,
is_torch_musa_available,
is_torch_neuroncore_available,
is_torch_npu_available,
is_torch_tf32_available,
is_torch_xla_available,
is_torch_xpu_available,
logging,
requires_backends,
)
from .utils.generic import strtobool
from .utils.import_utils import enable_tf32, is_optimum_neuron_available
logger = logging.get_logger(__name__)
log_levels = logging.get_log_levels_dict().copy()
trainer_log_levels = dict(**log_levels, passive=-1)
if is_torch_available():
import torch
import torch.distributed as dist
if is_accelerate_available():
from accelerate.state import AcceleratorState, PartialState
from accelerate.utils import DistributedType
from .trainer_pt_utils import AcceleratorConfig
if is_accelerate_available("1.10.1"):
from accelerate.parallelism_config import ParallelismConfig
else:
ParallelismConfig = Any
if is_torch_xla_available():
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"):
if is_optimum_neuron_available():
logger.info(
"Make sure that you are performing the training with the NeuronTrainer from optimum[neuron], this "
"will fail otherwise."
)
else:
logger.warning(
"Please use the NeuronTrainer from optimum[neuron] instead of the Transformers library to perform "
"training on AWS Trainium instances. More information here: "
"https://github.com/huggingface/optimum-neuron"
)
import torch_xla.distributed.xla_backend as xbn
if not isinstance(dist.group.WORLD, xbn.ProcessGroupXla):
dist.init_process_group(backend="xla")
if not isinstance(dist.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()
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") -> str | None:
"""
Returns the xla device type (CPU|GPU|TPU) or None if the device is a non-xla device.
"""
if is_torch_xla_available():
if device.type == "cpu":
return "CPU"
return xm.xla_real_devices([device])[0].split(":")[0]
return None
class OptimizerNames(ExplicitEnum):
"""
Stores the acceptable string identifiers for optimizers.
"""
ADAMW_TORCH = "adamw_torch"
ADAMW_TORCH_FUSED = "adamw_torch_fused"
ADAMW_TORCH_XLA = "adamw_torch_xla"
ADAMW_TORCH_NPU_FUSED = "adamw_torch_npu_fused"
ADAMW_APEX_FUSED = "adamw_apex_fused"
ADAFACTOR = "adafactor"
ADAMW_ANYPRECISION = "adamw_anyprecision"
ADAMW_TORCH_4BIT = "adamw_torch_4bit"
ADAMW_TORCH_8BIT = "adamw_torch_8bit"
ADEMAMIX = "ademamix"
SGD = "sgd"
ADAGRAD = "adagrad"
ADAMW_BNB = "adamw_bnb_8bit"
ADAMW_8BIT = "adamw_8bit" # just an alias for adamw_bnb_8bit
ADEMAMIX_8BIT = "ademamix_8bit"
LION_8BIT = "lion_8bit"
LION = "lion_32bit"
PAGED_ADAMW = "paged_adamw_32bit"
PAGED_ADAMW_8BIT = "paged_adamw_8bit"
PAGED_ADEMAMIX = "paged_ademamix_32bit"
PAGED_ADEMAMIX_8BIT = "paged_ademamix_8bit"
PAGED_LION = "paged_lion_32bit"
PAGED_LION_8BIT = "paged_lion_8bit"
RMSPROP = "rmsprop"
RMSPROP_BNB = "rmsprop_bnb"
RMSPROP_8BIT = "rmsprop_bnb_8bit"
RMSPROP_32BIT = "rmsprop_bnb_32bit"
GALORE_ADAMW = "galore_adamw"
GALORE_ADAMW_8BIT = "galore_adamw_8bit"
GALORE_ADAFACTOR = "galore_adafactor"
GALORE_ADAMW_LAYERWISE = "galore_adamw_layerwise"
GALORE_ADAMW_8BIT_LAYERWISE = "galore_adamw_8bit_layerwise"
GALORE_ADAFACTOR_LAYERWISE = "galore_adafactor_layerwise"
LOMO = "lomo"
ADALOMO = "adalomo"
GROKADAMW = "grokadamw"
SCHEDULE_FREE_RADAM = "schedule_free_radam"
SCHEDULE_FREE_ADAMW = "schedule_free_adamw"
SCHEDULE_FREE_SGD = "schedule_free_sgd"
APOLLO_ADAMW = "apollo_adamw"
APOLLO_ADAMW_LAYERWISE = "apollo_adamw_layerwise"
STABLE_ADAMW = "stable_adamw"
def _convert_str_dict(passed_value: dict):
"Safely checks that a passed value is a dictionary and converts any string values to their appropriate types."
for key, value in passed_value.items():
if isinstance(value, dict):
passed_value[key] = _convert_str_dict(value)
elif isinstance(value, str):
# First check for bool and convert
if value.lower() in ("true", "false"):
passed_value[key] = value.lower() == "true"
# Check for digit
elif value.isdigit():
passed_value[key] = int(value)
elif value.replace(".", "", 1).isdigit():
passed_value[key] = float(value)
return passed_value
@dataclass
class TrainingArguments:
"""
Configuration class for controlling all aspects of model training with the Trainer.
TrainingArguments centralizes all hyperparameters, optimization settings, logging preferences, and infrastructure choices needed for training.
[`HfArgumentParser`] 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`, *optional*, defaults to `"trainer_output"`):
The output directory where the model predictions and checkpoints will be written.
> Training Duration and Batch Size
per_device_train_batch_size (`int`, *optional*, defaults to 8):
The batch size *per device*. The **global batch size** is computed as:
`per_device_train_batch_size * number_of_devices` in multi-GPU or distributed setups.
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):
Overrides `num_train_epochs`. If set to a positive number, the total number of training steps to perform.
For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
`max_steps` is reached.
> Learning Rate & Scheduler
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for the optimizer. This is typically the peak learning rate when using a scheduler with warmup.
lr_scheduler_type (`str` or [`SchedulerType`], *optional*, defaults to `"linear"`):
The learning rate scheduler type to use. See [`SchedulerType`] for all possible values. Common choices:
- "linear" = [`get_linear_schedule_with_warmup`]
- "cosine" = [`get_cosine_schedule_with_warmup`]
- "constant" = [`get_constant_schedule`]
- "constant_with_warmup" = [`get_constant_schedule_with_warmup`]
lr_scheduler_kwargs (`dict` or `str`, *optional*, defaults to `None`):
The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
warmup_steps (`int` or `float`, *optional*, defaults to 0):
Number of steps for a linear warmup from 0 to `learning_rate`. Warmup helps stabilize training in the initial phase. Can be:
- An integer: exact number of warmup steps
- A float in range [0, 1): interpreted as ratio of total training steps
> Optimizer
optim (`str` or [`training_args.OptimizerNames`], *optional*, defaults to `"adamw_torch"` (for torch>=2.8 `"adamw_torch_fused"`)):
The optimizer to use. Common options:
- `"adamw_torch"`: PyTorch's AdamW (recommended default)
- `"adamw_torch_fused"`: Fused AdamW kernel
- `"adamw_hf"`: HuggingFace's AdamW implementation
- `"sgd"`: Stochastic Gradient Descent with momentum
- `"adafactor"`: Memory-efficient optimizer for large models
- `"adamw_8bit"`: 8-bit AdamW (requires bitsandbytes)
See [`OptimizerNames`] for the complete list.
optim_args (`str`, *optional*):
Optional arguments that are supplied to optimizers such as AnyPrecisionAdamW, AdEMAMix, and GaLore.
weight_decay (`float`, *optional*, defaults to 0):
Weight decay coefficient applied by the optimizer (not the loss function). Adds L2
regularization to prevent overfitting by penalizing large weights. Automatically
excluded from bias and LayerNorm parameters. Typical values: 0.01 (standard), 0.1
(stronger regularization), 0.0 (no regularization).
adam_beta1 (`float`, *optional*, defaults to 0.9):
The exponential decay rate for the first moment estimates (momentum) in Adam-based
optimizers. Controls how much history of gradients to retain.
adam_beta2 (`float`, *optional*, defaults to 0.999):
The exponential decay rate for the second moment estimates (variance) in Adam-based
optimizers. Controls adaptive learning rate scaling.
adam_epsilon (`float`, *optional*, defaults to 1e-8):
Epsilon value for numerical stability in Adam-based optimizers. Prevents division by
zero in the denominator of the update rule.
optim_target_modules (`Union[str, list[str]]`, *optional*):
The target modules to optimize, i.e. the module names that you would like to train.
Currently used for the [GaLore algorithm](https://huggingface.co/papers/2403.03507) and [APOLLO algorithm](https://huggingface.co/papers/2412.05270).
See [GaLore implementation](https://github.com/jiaweizzhao/GaLore) and [APOLLO implementation](https://github.com/zhuhanqing/APOLLO) for more details.
You need to make sure to pass a valid GaLore or APOLLO optimizer, e.g., one of: "apollo_adamw", "galore_adamw", "galore_adamw_8bit", "galore_adafactor" and make sure that the target modules are `nn.Linear` modules only.
> Regularization & Training Stability
gradient_accumulation_steps (`int`, *optional*, defaults to 1):
Number of update steps to accumulate gradients before performing a backward/update pass.
Simulates larger batch sizes without additional memory. Effective batch size =
`per_device_train_batch_size × num_devices × gradient_accumulation_steps`.
> [!TIP]
> When using gradient accumulation, one "step" is counted as one step with a backward pass. Therefore, logging, evaluation, and saving will occur every `gradient_accumulation_steps × xxx_step` training examples.
average_tokens_across_devices (`bool`, *optional*, defaults to `True`):
Whether or not to average tokens across devices. If enabled, will use all_reduce to synchronize
num_tokens_in_batch for precise loss calculation. Reference:
https://github.com/huggingface/transformers/issues/34242
max_grad_norm (`float`, *optional*, defaults to 1.0):
Maximum gradient norm for gradient clipping. Applied after backward pass, before
optimizer step. Prevents gradient explosion by scaling down gradients when their global
norm exceeds this threshold. Set to 0 to disable clipping. Typical values:
1.0 (standard), 0.5 (more conservative), 5.0 (less aggressive).
label_smoothing_factor (`float`, *optional*, defaults to 0.0):
Label smoothing factor to prevent overconfidence. Replaces hard 0/1 targets with soft
targets: 0 becomes `ε/num_labels` and 1 becomes `1 - ε + ε/num_labels`, where
ε = `label_smoothing_factor`. Zero means no smoothing. Typical range: 0.0 to 0.1.
> Mixed Precision Training
bf16 (`bool`, *optional*, defaults to `False`):
Enable bfloat16 (BF16) mixed precision training
Generally preferred over FP16 due to better numerical stability and no loss scaling required.
fp16 (`bool`, *optional*, defaults to `False`):
Enable float16 (FP16) mixed precision training.
Consider using BF16 instead if your hardware supports it.
bf16_full_eval (`bool`, *optional*, defaults to `False`):
Use full BF16 precision for evaluation (not just mixed precision). Faster and saves
memory but may affect metric values slightly. Only applies during evaluation.
fp16_full_eval (`bool`, *optional*, defaults to `False`):
Use full FP16 precision for evaluation (not just mixed precision). Faster and saves
memory but may affect metric values slightly. Only applies during evaluation.
tf32 (`bool`, *optional*):
Enable TensorFloat-32 (TF32) mode on Ampere and newer GPUs. TF32 uses 19-bit precision
for matrix multiplications (instead of FP32's 23-bit), providing up to 8x speedup with
negligible accuracy loss. Default depends on PyTorch version. See
[TF32 docs](https://huggingface.co/docs/transformers/perf_train_gpu_one#tf32).
> Gradient Checkpointing
gradient_checkpointing (`bool`, *optional*, defaults to `False`):
Enable gradient checkpointing to trade compute for memory. Reduces memory usage by
clearing activations during forward pass and recomputing them during backward pass.
Enables training larger models or batch sizes at the cost of ~20% slower training.
gradient_checkpointing_kwargs (`dict`, *optional*, defaults to `None`):
Keyword arguments passed to `gradient_checkpointing_enable()`.
> Compilation
torch_compile (`bool`, *optional*, defaults to `False`):
Compile the model using PyTorch 2.0's `torch.compile()` for faster training. Can provide
20-50% speedup with no code changes. Uses default compilation settings unless
`torch_compile_backend` or `torch_compile_mode` are specified.
torch_compile_backend (`str`, *optional*):
Backend for `torch.compile()`. If set, automatically enables `torch_compile`. Options
include `"inductor"` (default), `"aot_eager"`, `"cudagraphs"`. Backends vary by PyTorch
version - see PyTorch docs for available options.
torch_compile_mode (`str`, *optional*):
Compilation mode for `torch.compile()`. If set, automatically enables `torch_compile`.
Options: `"default"`, `"reduce-overhead"` (minimize Python overhead), `"max-autotune"`
(aggressive optimization, slower compile time).
> Kernels
use_liger_kernel (`bool`, *optional*, defaults to `False`):
Enable [Liger Kernel](https://github.com/linkedin/Liger-Kernel) optimizations. Increases
multi-GPU throughput by ~20% and reduces memory usage by ~60%. Works with Flash Attention,
FSDP, and DeepSpeed. Currently supports Llama, Mistral, Mixtral, and Gemma models.
liger_kernel_config (`Optional[dict]`, *optional*):
Configuration for Liger Kernel. Passed as kwargs to `_apply_liger_kernel_to_instance()`.
Options typically include: `"rope"`, `"swiglu"`, `"cross_entropy"`,
`"fused_linear_cross_entropy"`, `"rms_norm"`. If `None`, uses default configuration.
> Additional Optimizations
use_cache (`bool`, *optional*, defaults to `False`):
Whether or not to enable cache for the model. For training, this is usually not needed apart from some PEFT methods that uses `past_key_values`.
neftune_noise_alpha (`Optional[float]`):
If not `None`, this will activate NEFTune noise embeddings. This can drastically improve model performance
for instruction fine-tuning. Check out the [original paper](https://huggingface.co/papers/2310.05914) and the
[original code](https://github.com/neelsjain/NEFTune). Support transformers `PreTrainedModel` and also
`PeftModel` from peft. The original paper used values in the range [5.0, 15.0].
torch_empty_cache_steps (`int`, *optional*):
Number of steps to wait before calling `torch.<device>.empty_cache()`. If left unset or set to None, cache will not be emptied.
This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance](https://github.com/huggingface/transformers/issues/31372).
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.
> Logging & Monitoring Training
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_steps (`int` or `float`, *optional*, defaults to 500):
Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in
range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
logging_first_step (`bool`, *optional*, defaults to `False`):
Whether to log the first `global_step` or not.
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_nan_inf_filter (`bool`, *optional*, defaults to `True`):
Filter out NaN and Inf losses when logging. If `True`, replaces NaN/Inf losses with the
average of recent valid losses. Does not affect gradient computation, only logging.
include_num_input_tokens_seen (`Optional[Union[str, bool]]`, *optional*, defaults to "no"):
Whether to track the number of input tokens seen. Must be one of ["all", "non_padding", "no"] or a boolean value which map to "all" or "no".
May be slower in distributed training as gather operations must be called.
> Logging
log_level (`str`, *optional*, defaults to `passive`):
Logging level for the main process. Options: `"debug"`, `"info"`, `"warning"`, `"error"`,
`"critical"`, or `"passive"` (doesn't change the current Transformers logging level,
which defaults to `"warning"`)
log_level_replica (`str`, *optional*, defaults to `"warning"`):
Logging level for replica processes in distributed training. Same options as `log_level`.
disable_tqdm (`bool`, *optional*):
Disable tqdm progress bars. Defaults to `True` if `log_level` is warning or lower, `False` otherwise.
> Experiment Tracking Integration
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"`,
`"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"swanlab"`,
`"tensorboard"`, `"trackio"` and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"`
for no integrations.
run_name (`str`, *optional*):
A descriptor for the run. Typically used for [trackio](https://github.com/gradio-app/trackio),
[wandb](https://www.wandb.com/), [mlflow](https://www.mlflow.org/), [comet](https://www.comet.com/site) and
[swanlab](https://swanlab.cn) logging.
project (`str`, *optional*, defaults to `"huggingface"`):
The name of the project to use for logging. Currently, only used by Trackio.
trackio_space_id (`str` or `None`, *optional*, defaults to `"trackio"`):
The Hugging Face Space ID to deploy to when using Trackio. Should be a complete Space name like
`'username/reponame'` or `'orgname/reponame'`, or just `'reponame'` in which case the Space will be
created in the currently-logged-in Hugging Face user's namespace. If `None`, will log to a local directory.
Note that this Space will be public unless you set `hub_private_repo=True` or your organization's default
is to create private Spaces."
> Evaluation
eval_strategy (`str` or [`~trainer_utils.IntervalStrategy`], *optional*, defaults to `"no"`):
When to run evaluation. Options:
- `"no"`: No evaluation during training
- `"steps"`: Evaluate every `eval_steps`
- `"epoch"`: Evaluate at the end of each epoch
eval_steps (`int` or `float`, *optional*):
Number of update steps between two evaluations if `eval_strategy="steps"`. Will default to the same
value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1,
will be interpreted as ratio of total training steps.
eval_delay (`float`, *optional*):
Number of epochs or steps to wait for before the first evaluation can be performed, depending on the
eval_strategy.
per_device_eval_batch_size (`int`, *optional*, defaults to 8):
The batch size per device accelerator core/CPU for evaluation.
prediction_loss_only (`bool`, *optional*, defaults to `False`):
When performing evaluation and generating predictions, only returns the loss.
eval_on_start (`bool`, *optional*, defaults to `False`):
Whether to perform a evaluation step (sanity check) before the training to ensure the validation steps works correctly.
eval_do_concat_batches (`bool`, *optional*, defaults to `True`):
Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`,
will instead store them as lists, with each batch kept separate.
eval_use_gather_object (`bool`, *optional*, defaults to `False`):
Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices. This should only be enabled if users are not just returning tensors, and this is actively discouraged by PyTorch.
This is useful when the labels structure is non standard, like in computer vision tasks.
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 the device accelerator before being moved to the CPU (faster but
requires more memory).
> Metrics Computation
include_for_metrics (`list[str]`, *optional*, defaults to `[]`):
Include additional data in the `compute_metrics` function if needed for metrics computation.
Possible options to add to `include_for_metrics` list:
- `"inputs"`: Input data passed to the model, intended for calculating input dependent metrics.
- `"loss"`: Loss values computed during evaluation, intended for calculating loss dependent metrics.
batch_eval_metrics (`bool`, *optional*, defaults to `False`):
If set to `True`, evaluation will call compute_metrics at the end of each batch to accumulate statistics
rather than saving all eval logits in memory. When set to `True`, you must pass a compute_metrics function
that takes a boolean argument `compute_result`, which when passed `True`, will trigger the final global
summary statistics from the batch-level summary statistics you've accumulated over the evaluation set.
> Checkpointing & Saving
save_only_model (`bool`, *optional*, defaults to `False`):
Save only model weights, not optimizer/scheduler/RNG state. Significantly reduces
checkpoint size but prevents resuming training from the checkpoint. Use when you only
need the trained model for inference, not continued training.
You can only load the model using `from_pretrained` with this option set to `True`.
save_strategy (`str` or [`~trainer_utils.SaveStrategy`], *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`.
- `"best"`: Save is done whenever a new `best_metric` is achieved.
save_steps (`int` or `float`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a
float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
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.
save_total_limit (`int`, *optional*):
Maximum number of checkpoints to keep. Deletes older checkpoints in `output_dir`. When
`load_best_model_at_end=True`, the best checkpoint is always retained plus the most
recent ones. For example, `save_total_limit=5` keeps the 4 most recent plus the best
enable_jit_checkpoint (`bool`, *optional*, defaults to `False`):
Enable Just-In-Time checkpointing on SIGTERM signal for graceful termination on
preemptible workloads. **Important**: Configure your orchestrator's graceful shutdown
period to allow sufficient time. For Kubernetes, set `terminationGracePeriodSeconds`
(default 30s is usually insufficient). For Slurm, use `--signal=USR1@<seconds>`.
Required grace period ≥ longest iteration time + checkpoint save time.
> Hugging Face Hub Integration
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.
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
`hf auth login`.
hub_private_repo (`bool`, *optional*):
Whether to make the repo private. If `None` (default), the repo will be public unless the organization's
default is private. This value is ignored if the repo already exists. If reporting to Trackio with
deployment to Hugging Face Spaces enabled, the same logic determines whether the Space is private.
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`.
hub_strategy (`str` or [`~trainer_utils.HubStrategy`], *optional*, defaults to `"every_save"`):
Defines what and when to push to Hub. Options:
- `"end"`: Push only at the end of training
- `"every_save"`: Push on each save (async to not block training)
- `"checkpoint"`: Like `"every_save"` plus push latest checkpoint to `"last-checkpoint"` subfolder for easy resuming
- `"all_checkpoints"`: Push all checkpoints as they appear
hub_always_push (`bool`, *optional*, defaults to `False`):
Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished.
hub_revision (`str`, *optional*):
The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash.
> Best Model Tracking
load_best_model_at_end (`bool`, *optional*, defaults to `False`):
Load the best checkpoint at the end of training. Requires `eval_strategy` to be set.
When enabled, the best checkpoint is always saved (see `save_total_limit`).
<Tip>
When `True`, `save_strategy` must match `eval_strategy`, and if using `"steps"`,
`save_steps` must be a multiple of `eval_steps`.
</Tip>
metric_for_best_model (`str`, *optional*):
Metric to use for comparing models when `load_best_model_at_end=True`. Must be a metric
name returned by evaluation, with or without the `"eval_"` prefix. Defaults to `"loss"`.
If you set this, `greater_is_better` will default to `True` unless the name ends with
`"loss"`. Examples: `"accuracy"`, `"f1"`, `"eval_bleu"`.
greater_is_better (`bool`, *optional*):
Whether higher metric values are better. Defaults based on `metric_for_best_model`:
`True` if the metric name doesn't end in `"loss"`, `False` otherwise.
> Resuming Training
ignore_data_skip (`bool`, *optional*, defaults to `False`):
When resuming training, skip fast-forwarding through the dataset to reach the previous
state. If `True`, training starts from the beginning of the dataset (faster resume but
results won't match interrupted training). If `False`, skips seen data (slower resume
but exact continuation).
restore_callback_states_from_checkpoint (`bool`, *optional*, defaults to `False`):
Restore callback states from checkpoint when resuming. If `True`, will override callbacks
passed to Trainer if they exist in the checkpoint.
> Reproducibility
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. Important: this will negatively impact the performance, so only use it for debugging.
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.
> Hardware Configuration
use_cpu (`bool`, *optional*, defaults to `False`):
Whether or not to use cpu. If set to False, we will use the available torch device/backend.
> Accelerate Configuration
accelerator_config (`str`, `dict`, or `AcceleratorConfig`, *optional*):
Configuration for the internal Accelerate integration. Can be:
- Path to JSON config file: `"accelerator_config.json"`
- Dictionary with config options
- `AcceleratorConfig` instance
Key options:
- `split_batches` (`bool`, defaults to `False`): Whether to split batches across devices.
If `True`, actual batch size is the same on all devices (total must be divisible by
num_processes). If `False`, each device gets the specified batch size.
- `dispatch_batches` (`bool`): If `True`, only main process iterates through dataloader
and dispatches batches to devices. Defaults to `True` for `IterableDataset`, `False`
otherwise.
- `even_batches` (`bool`, defaults to `True`): Duplicate samples from dataset start to
ensure all workers get equal batch sizes.
- `use_seedable_sampler` (`bool`, defaults to `True`): Use fully seedable random sampler
for reproducibility.
- `use_configured_state` (`bool`, defaults to `False`): Use pre-initialized
`AcceleratorState`/`PartialState` instead of creating new one. May cause issues with
hyperparameter tuning.
parallelism_config (`ParallelismConfig`, *optional*):
Parallelism configuration for the training run. Requires Accelerate `1.10.1`
> Dataloader
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.
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.
dataloader_pin_memory (`bool`, *optional*, defaults to `True`):
Whether you want to pin memory in data loaders or not. Will default to `True`.
dataloader_persistent_workers (`bool`, *optional*, defaults to `False`):
If True, the data loader will not shut down the worker processes after a dataset has been consumed once.
This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will
increase RAM usage. Will default to `False`.
dataloader_prefetch_factor (`int`, *optional*):
Number of batches loaded in advance by each worker.
2 means there will be a total of 2 * num_workers batches prefetched across all workers.
remove_unused_columns (`bool`, *optional*, defaults to `True`):
Whether or not to automatically remove the columns unused by the model forward method.
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.
You should only specify `label_names` if you're using custom label names or if your model's `forward` consumes multiple label tensors (e.g., extractive QA).
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`.
> DDP (DistributedDataParallel)
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`.
ddp_broadcast_buffers (`bool`, *optional*):
When using distributed training, the value of the flag `broadcast_buffers` passed to
`DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
ddp_backend (`str`, *optional*):
The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"xccl"`, `"gloo"`, `"hccl"`.
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 to the [PyTorch documentation](https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more
information.
> FSDP (Fully Sharded Data Parallel)
fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `None`):
Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Options:
- `"full_shard"`: Shard parameters, gradients, and optimizer states (most memory efficient)
- `"shard_grad_op"`: Shard only optimizer states and gradients (ZeRO-2)
- `"hybrid_shard"`: Full shard within nodes, replicate across nodes
- `"hybrid_shard_zero2"`: Shard gradients/optimizer within nodes, replicate across nodes
- `"offload"`: Offload parameters and gradients to CPU (only with `"full_shard"` or
`"shard_grad_op"`)
- `"auto_wrap"`: Automatically wrap layers 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
fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.
A List of config and its options:
- fsdp_version (`int`, *optional*, defaults to `1`):
The version of FSDP to use. Defaults to 1.
- 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).
- 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).
- 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_post"` : This prefetches the next set of parameters after the current set of
parameter's gradient computation.
- 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.
- use_orig_params (`bool`, *optional*, defaults to `True`)
If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
frozen and trainable parameters. Useful in cases such as parameter-efficient fine-tuning. Please
refer this
[blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
- sync_module_states (`bool`, *optional*, defaults to `True`)
If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
ensure they are the same across all ranks after initialization
- cpu_ram_efficient_loading (`bool`, *optional*, defaults to `False`)
If `"True"`, only the first process loads the pretrained model checkpoint while all other processes
have empty weights. When this setting as `"True"`, `sync_module_states` also must to be `"True"`,
otherwise all the processes except the main process would have random weights leading to unexpected
behaviour during training.
- activation_checkpointing (`bool`, *optional*, defaults to `False`):
If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of
certain layers and recomputing them during a backward pass. Effectively, this trades extra
computation time for reduced memory usage.
- 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
deepspeed (`str` or `dict`, *optional*):
Enable [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) integration. Value is either:
- Path to DeepSpeed JSON config file: `"ds_config.json"`
- Loaded config as dictionary
> [!TIP]
> If using ZeRO initialization, instantiate your model *after* initializing
`TrainingArguments`, otherwise ZeRO won't be applied.
> Debugging & Profiling (Experimental)
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
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.
> External Script Flags (not used by Trainer)
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 `eval_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.
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.
"""
# Sometimes users will pass in a `str` repr of a dict in the CLI
# We need to track what fields those can be. Each time a new arg
# has a dict type, it must be added to this list.
# Important: These should be typed with Optional[Union[dict,str,...]]
_VALID_DICT_FIELDS = [
"accelerator_config",
"fsdp_config",
"deepspeed",
"gradient_checkpointing_kwargs",
"lr_scheduler_kwargs",
]
output_dir: str | None = field(
default=None,
metadata={
"help": "The output directory where the model predictions and checkpoints will be written. Defaults to 'trainer_output' if not provided."
},
)
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."})
eval_strategy: 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."},
)
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per device accelerator core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per device accelerator 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: int | None = field(
default=None,
metadata={"help": "Number of predictions steps to accumulate before moving the tensors to the CPU."},
)
eval_delay: float = field(
default=0,
metadata={
"help": (
"Number of epochs or steps to wait for before the first evaluation can be performed, depending on the"
" eval_strategy."
)
},
)
torch_empty_cache_steps: int | None = field(
default=None,
metadata={
"help": "Number of steps to wait before calling `torch.<device>.empty_cache()`."
"This can help avoid CUDA out-of-memory errors by lowering peak VRAM usage at a cost of about [10% slower performance](https://github.com/huggingface/transformers/issues/31372)."
"If left unset or set to None, cache will not be emptied."
},
)
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: SchedulerType | str = field(
default="linear",
metadata={"help": "The scheduler type to use."},
)
lr_scheduler_kwargs: dict | str | None = field(
default=None,
metadata={
"help": (
"Extra parameters for the lr_scheduler such as {'num_cycles': 1} for the cosine with hard restarts."
)
},
)
warmup_ratio: float | None = field(
default=None,
metadata={
"help": "This argument is deprecated and will be removed in v5.2. Use `warmup_steps` instead as it also works with float values."
},
)
warmup_steps: float = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
log_level: 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: 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: str | None = field(
default=None,
metadata={
"help": "Deprecated and will be removed in v5.2. Set env var `TENSORBOARD_LOGGING_DIR` instead. TensorBoard log directory."
},
)
logging_strategy: 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: float = field(
default=500,
metadata={
"help": (
"Log every X updates steps. Should be an integer or a float in range `[0,1)`. "
"If smaller than 1, will be interpreted as ratio of total training steps."
)
},
)
logging_nan_inf_filter: bool = field(default=True, metadata={"help": "Filter nan and inf losses for logging."})
save_strategy: SaveStrategy | str = field(
default="steps",
metadata={"help": "The checkpoint save strategy to use."},
)
save_steps: float = field(
default=500,
metadata={
"help": (
"Save checkpoint every X updates steps. Should be an integer or a float in range `[0,1)`. "
"If smaller than 1, will be interpreted as ratio of total training steps."
)
},
)
save_total_limit: int | None = field(
default=None,
metadata={
"help": (
"If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in"
" `output_dir`. When `load_best_model_at_end` is enabled, the 'best' checkpoint according to"
" `metric_for_best_model` will always be retained in addition to the most recent ones. For example,"
" for `save_total_limit=5` and `load_best_model_at_end=True`, the four last checkpoints will always be"
" retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end=True`,"
" it is possible that two checkpoints are saved: the last one and the best one (if they are different)."
" Default is unlimited checkpoints."
)
},
)
enable_jit_checkpoint: bool = field(
default=False,
metadata={
"help": (
"Whether to enable Just-In-Time (JIT) checkpointing on SIGTERM signal. "
"When enabled, training will checkpoint upon receiving SIGTERM, "
"allowing for graceful termination without losing progress. "
"This is particularly useful for shared clusters with preemptible workloads (Kueue). "
"IMPORTANT: You must configure your orchestrator's graceful shutdown period. "
"Kubernetes: set terminationGracePeriodSeconds (default 30s is insufficient!) in your job definition. "
"Slurm: use --signal=USR1@<seconds> in sbatch to send SIGTERM before time limit. "
"Calculate required grace period as: iteration time + checkpoint saving time. "
"Example: 2min iteration + 2min checkpoint = 240 seconds minimum."
)
},
)
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"
)
},
)
save_only_model: bool = field(
default=False,
metadata={
"help": (
"When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state."
"Note that when this is true, you won't be able to resume training from checkpoint."
"This enables you to save storage by not storing the optimizer, scheduler & rng state."
"You can only load the model using from_pretrained with this option set to True."
)
},
)
restore_callback_states_from_checkpoint: bool = field(
default=False,
metadata={
"help": "Whether to restore the callback states from the checkpoint. If `True`, will override callbacks passed to the `Trainer` if they exist in the checkpoint."
},
)
use_cpu: bool = field(
default=False,
metadata={
"help": "Whether or not to use cpu. If left to False, we will use the available torch device/backend (cuda/mps/xpu/hpu etc.)"
},
)
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
data_seed: int | None = field(default=None, metadata={"help": "Random seed to be used with data samplers."})
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 (use_cpu) or Ascend NPU. 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"},
)
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: bool | None = 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": "When using torch.distributed.launch (Deprecated), it will pass `local_rank` in the script, so we need this for the parser. To get the local rank, prefer using the property `local_process_index`"
},
)
ddp_backend: str | None = field(
default=None,
metadata={
"help": "The backend to be used for distributed training",
"choices": ["nccl", "gloo", "mpi", "xccl", "hccl", "cncl", "mccl"],
},
)
debug: str | list[DebugOption] = 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: float | None = field(
default=None,
metadata={
"help": (
"Run an evaluation every X steps. Should be an integer or a float in range `[0,1)`. "
"If smaller than 1, will be interpreted as ratio of total training 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."
)
},
)
dataloader_prefetch_factor: int | None = field(
default=None,
metadata={
"help": (
"Number of batches loaded in advance by each worker. "
"2 means there will be a total of 2 * num_workers batches prefetched across all workers. "
)
},
)
run_name: str | None = field(
default=None,
metadata={
"help": (
"An optional descriptor for the run. Notably used for trackio, wandb, mlflow comet and swanlab "
"logging."
)
},
)
disable_tqdm: bool | None = field(
default=None, metadata={"help": "Whether or not to disable the tqdm progress bars."}
)
remove_unused_columns: bool = field(
default=True, metadata={"help": "Remove columns not required by the model when using an nlp.Dataset."}
)
label_names: list[str] | None = field(
default=None, metadata={"help": "The list of keys in your dictionary of inputs that correspond to the labels."}
)
load_best_model_at_end: bool = field(
default=False,
metadata={
"help": (
"Whether or not to load the best model found during training at the end of training. When this option"
" is enabled, the best checkpoint will always be saved. See `save_total_limit` for more."
)
},
)
metric_for_best_model: str | None = field(
default=None, metadata={"help": "The metric to use to compare two different models."}
)
greater_is_better: bool | None = 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."
)
},
)
fsdp: list[FSDPOption] | str | None = field(
default=None,
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_config: dict[str, Any] | str | None = 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`."
)
},
)
accelerator_config: dict | str | None = field(
default=None,
metadata={
"help": (
"Config to be used with the internal Accelerator object initialization. The value is either a "
"accelerator json config file (e.g., `accelerator_config.json`) or an already loaded json file as `dict`."
)
},
)
parallelism_config: ParallelismConfig | None = field(
default=None,
metadata={"help": ("Parallelism configuration for the training run. Requires Accelerate `1.12.0`")},
)
deepspeed: dict | str | None = 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_torch"
if is_torch_available():
from .pytorch_utils import is_torch_greater_or_equal_than_2_8
if is_torch_greater_or_equal_than_2_8:
default_optim = "adamw_torch_fused"
optim: OptimizerNames | str = field(
default=default_optim,
metadata={"help": "The optimizer to use."},
)
optim_args: str | None = field(default=None, metadata={"help": "Optional arguments to supply to optimizer."})
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: str = field(
default="length",
metadata={"help": "Column name with precomputed lengths to use when grouping by length."},
)
report_to: None | str | list[str] = field(
default="none", metadata={"help": "The list of integrations to report the results and logs to."}
)
project: str = field(
default="huggingface",
metadata={"help": "The name of the project to use for logging. Currenly, only used by Trackio."},
)
trackio_space_id: str | None = field(
default="trackio",
metadata={
"help": "The Hugging Face Space ID to deploy to when using Trackio. Should be a complete Space name like "
"'username/reponame' or 'orgname/reponame', or just 'reponame' in which case the Space will be created in "
"the currently-logged-in Hugging Face user's namespace. If `None`, will log to a local directory. Note "
"that this Space will be public unless you set `hub_private_repo=True` or your organization's "
"default is to create private Spaces."
},
)
ddp_find_unused_parameters: bool | None = field(
default=None,
metadata={
"help": (
"When using distributed training, the value of the flag `find_unused_parameters` passed to "
"`DistributedDataParallel`."
)
},
)
ddp_bucket_cap_mb: int | None = field(
default=None,
metadata={
"help": (
"When using distributed training, the value of the flag `bucket_cap_mb` passed to "
"`DistributedDataParallel`."
)
},
)
ddp_broadcast_buffers: bool | None = field(
default=None,
metadata={
"help": (
"When using distributed training, the value of the flag `broadcast_buffers` passed to "
"`DistributedDataParallel`."
)
},
)
dataloader_pin_memory: bool = field(
default=True, metadata={"help": "Whether or not to pin memory for DataLoader."}
)
dataloader_persistent_workers: bool = field(
default=False,
metadata={
"help": "If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage."
},
)
skip_memory_metrics: bool = field(
default=True, metadata={"help": "Whether or not to skip adding of memory profiler reports to metrics."}
)
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: str | None = field(
default=None,
metadata={"help": "The path to a folder with a valid checkpoint for your model."},
)
hub_model_id: str | None = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
)
hub_strategy: HubStrategy | str = field(
default="every_save",
metadata={"help": "The hub strategy to use when `--push_to_hub` is activated."},
)
hub_token: str | None = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
hub_private_repo: bool | None = field(
default=None,
metadata={
"help": "Whether to make the repo private. If `None` (default), the repo will be public unless the "
"organization's default is private. This value is ignored if the repo already exists. If reporting to "
"Trackio with deployment to Hugging Face Spaces enabled, the same logic determines whether the Space is "
"private."
},
)
hub_always_push: bool = field(
default=False,
metadata={"help": "Unless `True`, the Trainer will skip pushes if the previous one wasn't finished yet."},
)
hub_revision: str | None = field(
default=None,
metadata={
"help": "The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash."
},
)
gradient_checkpointing: bool = field(
default=False,
metadata={
"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass."
},
)
gradient_checkpointing_kwargs: dict[str, Any] | str | None = field(
default=None,
metadata={
"help": "Gradient checkpointing key word arguments such as `use_reentrant`. Will be passed to `torch.utils.checkpoint.checkpoint` through `model.gradient_checkpointing_enable`."
},
)
include_for_metrics: list[str] = field(
default_factory=list,
metadata={
"help": "List of strings to specify additional data to include in the `compute_metrics` function."
"Options: 'inputs', 'loss'."
},
)
eval_do_concat_batches: bool = field(
default=True,
metadata={
"help": "Whether to recursively concat inputs/losses/labels/predictions across batches. If `False`, will instead store them as lists, with each batch kept separate."
},
)
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. Important: this will negatively impact the performance, so only use it for debugging."
)
},
)
ddp_timeout: 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: str | None = field(
default=None,
metadata={
"help": "Which backend to use with `torch.compile`, passing one will trigger a model compilation.",
},
)
torch_compile_mode: str | None = field(
default=None,
metadata={
"help": "Which mode to use with `torch.compile`, passing one will trigger a model compilation.",
},
)
include_num_input_tokens_seen: str | bool = field(
default="no",
metadata={
"help": (
"Whether to track the number of input tokens seen. "
"Must be one of [`all`, `non_padding`, `no`] or a boolean value which map to `all` or `no`"
)
},
)
neftune_noise_alpha: float | None = field(
default=None,
metadata={
"help": "Activates neftune noise embeddings into the model. NEFTune has been proven to drastically improve model performances for instruction fine-tuning. Check out the original paper here: https://huggingface.co/papers/2310.05914 and the original code here: https://github.com/neelsjain/NEFTune. Only supported for `PreTrainedModel` and `PeftModel` classes."
},
)
optim_target_modules: None | str | list[str] = field(
default=None,
metadata={
"help": "Target modules for the optimizer defined in the `optim` argument. Only used for the GaLore optimizer at the moment."
},
)
batch_eval_metrics: bool = field(
default=False,
metadata={"help": "Break eval metrics calculation into batches to save memory."},
)
eval_on_start: bool = field(
default=False,
metadata={
"help": "Whether to run through the entire `evaluation` step at the very beginning of training as a sanity check."
},
)
use_liger_kernel: bool = field(
default=False,
metadata={"help": "Whether or not to enable the Liger Kernel for model training."},
)
liger_kernel_config: dict[str, bool] | None = field(
default=None,
metadata={
"help": (
"Configuration to be used for Liger Kernel. When use_liger_kernel=True, "
"this dict is passed as keyword arguments to the `_apply_liger_kernel_to_instance` function, "
"which specifies which kernels to apply. Available options vary by model "
"but typically include: 'rope', 'swiglu', 'cross_entropy', 'fused_linear_cross_entropy', "
"'rms_norm', etc. If None, use the default kernel configurations."
)
},
)
eval_use_gather_object: bool = field(
default=False,
metadata={
"help": "Whether to run recursively gather object in a nested list/tuple/dictionary of objects from all devices."
},
)
average_tokens_across_devices: bool = field(
default=True,
metadata={
"help": "Whether or not to average tokens across devices. If enabled, will use all_reduce to "
"synchronize num_tokens_in_batch for precise loss calculation. Reference: "
"https://github.com/huggingface/transformers/issues/34242"
},
)
use_cache: bool = field(
default=False,
metadata={
"help": "Whether or not to use cache for the model For training, this is usually not needed apart from some PEFT methods that uses `past_key_values`."
},
)
def __post_init__(self):
# Set default output_dir if not provided
if self.output_dir is None:
self.output_dir = "trainer_output"
logger.info(
"No output directory specified, defaulting to 'trainer_output'. "
"To change this behavior, specify --output_dir when creating TrainingArguments."
)
# Parse in args that could be `dict` sent in from the CLI as a string
for valid_field in self._VALID_DICT_FIELDS:
passed_value = getattr(self, valid_field)
# We only want to do this if the str starts with a bracket to indicate a `dict`
# else its likely a filename if supported
if isinstance(passed_value, str) and passed_value.startswith("{"):
loaded_dict = json.loads(passed_value)
# Convert str values to types if applicable
loaded_dict = _convert_str_dict(loaded_dict)
setattr(self, valid_field, loaded_dict)
# 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.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
self.eval_strategy = IntervalStrategy(self.eval_strategy)
self.logging_strategy = IntervalStrategy(self.logging_strategy)
self.save_strategy = SaveStrategy(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.eval_strategy != IntervalStrategy.NO:
self.do_eval = True
if self.torch_empty_cache_steps is not None:
if not (isinstance(self.torch_empty_cache_steps, int) and self.torch_empty_cache_steps > 0):
raise ValueError(
f"`torch_empty_cache_steps` must be an integer bigger than 0, got {self.torch_empty_cache_steps}."
)
# eval_steps has to be defined and non-zero, falls back to logging_steps if the latter is non-zero
if self.eval_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.eval_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")
if self.logging_strategy == IntervalStrategy.STEPS and self.logging_steps > 1:
if self.logging_steps != int(self.logging_steps):
raise ValueError(f"--logging_steps must be an integer if bigger than 1: {self.logging_steps}")
self.logging_steps = int(self.logging_steps)
if self.eval_strategy == IntervalStrategy.STEPS and self.eval_steps > 1:
if self.eval_steps != int(self.eval_steps):
raise ValueError(f"--eval_steps must be an integer if bigger than 1: {self.eval_steps}")
self.eval_steps = int(self.eval_steps)
if self.save_strategy == SaveStrategy.STEPS and self.save_steps > 1:
if self.save_steps != int(self.save_steps):
raise ValueError(f"--save_steps must be an integer if bigger than 1: {self.save_steps}")
self.save_steps = int(self.save_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 and self.save_strategy != SaveStrategy.BEST:
if self.eval_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.eval_strategy}\n- Save strategy: {self.save_strategy}"
)
if self.eval_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
if self.eval_steps < 1 or self.save_steps < 1:
if not (self.eval_steps < 1 and self.save_steps < 1):
raise ValueError(
"--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
"steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps "
f"{self.save_steps} and eval_steps {self.eval_steps}."
)
# Work around floating point precision issues
LARGE_MULTIPLIER = 1_000_000
if (self.save_steps * LARGE_MULTIPLIER) % (self.eval_steps * LARGE_MULTIPLIER) != 0:
raise ValueError(
"--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
f"steps, but found {self.save_steps}, which is not a multiple of {self.eval_steps}."
)
else:
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 or self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU
) 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 = not self.metric_for_best_model.endswith("loss")
if is_torch_available():
if self.bf16 or self.bf16_full_eval:
if (
not self.use_cpu and not is_torch_bf16_gpu_available() and not is_torch_xla_available()
): # added for tpu support
error_message = "Your setup doesn't support bf16/gpu. You need to assign use_cpu if you want to train the model on CPU"
if is_torch_cuda_available():
error_message += " You need Ampere+ GPU with cuda>=11.0"
# gpu
raise ValueError(error_message)
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.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU:
if self.eval_strategy == IntervalStrategy.NO:
raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires an eval strategy")
if not is_torch_available():
raise ValueError("lr_scheduler_type reduce_lr_on_plateau requires torch>=0.2.0")
self.optim = OptimizerNames(self.optim)
# We need to get from the env as we are setting deepspeed mixed precison afterwards
self.mixed_precision = os.environ.get("ACCELERATE_MIXED_PRECISION", "no")
if self.fp16:
self.mixed_precision = "fp16"
elif self.bf16:
self.mixed_precision = "bf16"
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:
if not self.use_cpu and is_torch_hpu_available():
self.torch_compile_backend = "hpu_backend"
else:
self.torch_compile_backend = "inductor"
if self.torch_compile:
# TODO: remove this once we've bumped the minimum accelerate version
if not is_accelerate_available("1.2.0"):
os.environ["ACCELERATE_DYNAMO_BACKEND"] = self.torch_compile_backend
if self.torch_compile_mode is not None:
os.environ["ACCELERATE_DYNAMO_MODE"] = self.torch_compile_mode
# We need to setup the accelerator config here *before* the first call to `self.device`
if is_accelerate_available():
if not isinstance(self.accelerator_config, AcceleratorConfig):
if self.accelerator_config is None:
self.accelerator_config = AcceleratorConfig()
elif isinstance(self.accelerator_config, dict):
self.accelerator_config = AcceleratorConfig(**self.accelerator_config)
# Check that a user didn't pass in the class instantiator
# such as `accelerator_config = AcceleratorConfig`
elif isinstance(self.accelerator_config, type):
raise NotImplementedError(
"Tried passing in a callable to `accelerator_config`, but this is not supported. "
"Please pass in a fully constructed `AcceleratorConfig` object instead."
)
else:
self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
if self.accelerator_config.split_batches:
logger.info(
"Using `split_batches=True` in `accelerator_config` will override the `per_device_train_batch_size` "
"Batches will be split across all processes equally when using `split_batches=True`."
)
# Initialize device before we proceed
if is_torch_available():
self.device
if is_torch_available() and self.torch_compile:
if is_torch_tf32_available():
if self.tf32 is None and not self.fp16 or self.bf16:
device_str = "MUSA" if is_torch_musa_available() else "CUDA"
logger.info(
f"Setting TF32 in {device_str} backends to speedup torch compile, you won't see any improvement"
" otherwise."
)
enable_tf32(True)
else:
logger.warning(
"The speedups for torchdynamo mostly come with GPU Ampere or higher and which is not detected here."
)
if is_torch_available() and self.tf32 is not None:
if self.tf32:
if is_torch_tf32_available():
enable_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():
enable_tf32(False)
# no need to assert on else
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()
if "codecarbon" in self.report_to and torch.version.hip:
logger.warning(
"When using the Trainer, CodeCarbonCallback requires the `codecarbon` package, which is not compatible with AMD ROCm (https://github.com/mlco2/codecarbon/pull/490). Automatically disabling the codecarbon callback. Reference: https://huggingface.co/docs/transformers/v4.39.3/en/main_classes/trainer#transformers.TrainingArguments.report_to."
)
self.report_to.remove("codecarbon")
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 is not None:
logger.warning("warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.")
self.warmup_steps = self.warmup_ratio
if self.warmup_steps < 0:
raise ValueError("warmup_steps must be an integer or a float")
if isinstance(self.debug, str):
self.debug = [DebugOption(s) for s in self.debug.split()]
elif self.debug is None:
self.debug = []
self.fsdp_plugin_args = None
# we can't save the plugin due to a pickle issue, so we do not initialize the plugin here
self.fsdp_plugin_args = self._process_fsdp_args()
self.deepspeed_plugin = None
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(
f"--deepspeed requires Accelerate to be installed: `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`."
)
from transformers.integrations.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)
# Accelerate DeepSpeed Plugin
from accelerate.utils import DeepSpeedPlugin
self.deepspeed_plugin = DeepSpeedPlugin(hf_ds_config=self.hf_deepspeed_config)
elif strtobool(os.environ.get("ACCELERATE_USE_DEEPSPEED", "false")):
# Accelerate DeepSpeed Plugin
from accelerate.utils import DeepSpeedPlugin
self.deepspeed_plugin = DeepSpeedPlugin()
self.deepspeed_plugin.set_mixed_precision(self.mixed_precision)
self.deepspeed_plugin.set_deepspeed_weakref()
if self.use_cpu:
self.dataloader_pin_memory = False
if self.dataloader_num_workers == 0 and self.dataloader_prefetch_factor is not None:
raise ValueError(
"--dataloader_prefetch_factor can only be set when data is loaded in a different process, i.e."
" when --dataloader_num_workers > 1."
)
if isinstance(self.include_num_input_tokens_seen, bool):
self.include_num_input_tokens_seen = "all" if self.include_num_input_tokens_seen else "no"
if self.logging_dir is not None:
logger.warning(
"`logging_dir` is deprecated and will be removed in v5.2. Please set `TENSORBOARD_LOGGING_DIR` instead."
)
def __str__(self):
self_as_dict = asdict(self)
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.
"""
train_batch_size = self.per_device_train_batch_size * max(1, self.n_gpu)
return train_batch_size
@property
def eval_batch_size(self) -> int:
"""
The actual batch size for evaluation.
"""
eval_batch_size = self.per_device_eval_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 not is_sagemaker_mp_enabled():
if not is_accelerate_available():
raise ImportError(
f"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: "
f"Please run `pip install transformers[torch]` or `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
)
# We delay the init of `PartialState` to the end for clarity
accelerator_state_kwargs: dict[str, Any] = {"enabled": True, "use_configured_state": False}
if isinstance(self.accelerator_config, AcceleratorConfig):
accelerator_state_kwargs["use_configured_state"] = self.accelerator_config.pop(
"use_configured_state", False
)
if accelerator_state_kwargs["use_configured_state"]:
if PartialState._shared_state == {}:
raise ValueError(
"Passing `'use_configured_state':True` to the AcceleratorConfig requires a pre-configured "
"`AcceleratorState` or `PartialState` to be defined before calling `TrainingArguments`. "
)
# We rely on `PartialState` to yell if there's issues here (which it will)
self.distributed_state = PartialState(cpu=self.use_cpu)
if self.deepspeed and self.distributed_state.distributed_type != DistributedType.DEEPSPEED:
raise RuntimeError(
"Tried to use an already configured `Accelerator` or `PartialState` that was not initialized for DeepSpeed, "
"but also passed in a `deepspeed` configuration to the `TrainingArguments`. Please set "
"`use_configured_state:False` instead or setup your `Accelerator` or `PartialState` properly."
)
else:
AcceleratorState._reset_state(reset_partial_state=True)
self.distributed_state = None
self._n_gpu = 1
if self.use_cpu or strtobool(os.environ.get("ACCELERATE_USE_CPU", "False")):
accelerator_state_kwargs["cpu"] = True
accelerator_state_kwargs["backend"] = self.ddp_backend
self._n_gpu = 0
elif is_sagemaker_mp_enabled():
accelerator_state_kwargs["enabled"] = False
device = torch.device("cuda", smp.local_rank())
torch.cuda.set_device(device)
elif is_sagemaker_dp_enabled():
accelerator_state_kwargs["_use_sagemaker_dp"] = True
elif self.deepspeed:
accelerator_state_kwargs["use_deepspeed"] = True
accelerator_state_kwargs["timeout"] = timedelta(seconds=self.ddp_timeout)
else:
accelerator_state_kwargs["backend"] = self.ddp_backend
accelerator_state_kwargs["timeout"] = timedelta(seconds=self.ddp_timeout)
# Now we pop everything
if accelerator_state_kwargs.pop("enabled", False) and not accelerator_state_kwargs.pop(
"use_configured_state", False
):
# We need to patch this env var when enabling to detect deepspeed
use_deepspeed = accelerator_state_kwargs.pop("use_deepspeed", False)
if use_deepspeed:
os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
self.distributed_state = PartialState(**accelerator_state_kwargs)
if use_deepspeed:
del os.environ["ACCELERATE_USE_DEEPSPEED"]
if not is_sagemaker_mp_enabled():
device = self.distributed_state.device
if dist.is_available() and dist.is_initialized() and self.parallel_mode != ParallelMode.DISTRIBUTED:
logger.warning(
"torch.distributed process group is initialized, but parallel_mode != ParallelMode.DISTRIBUTED. "
"In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"
)
if is_torch_xla_available():
device = self.distributed_state.device
self._n_gpu = 0
elif is_sagemaker_dp_enabled() or is_sagemaker_mp_enabled():
# Already set _n_gpu
pass
elif self.distributed_state.distributed_type == DistributedType.NO:
if self.use_cpu:
device = torch.device("cpu")
elif is_torch_mps_available():
device = torch.device("mps")
elif is_torch_xpu_available():
device = torch.device("xpu:0")
torch.xpu.set_device(device)
elif is_torch_mlu_available():
device = torch.device("mlu:0")
torch.mlu.set_device(device)
elif is_torch_musa_available():
device = torch.device("musa:0")
torch.musa.set_device(device)
elif is_torch_npu_available():
device = torch.device("npu:0")
torch.npu.set_device(device)
elif is_torch_hpu_available():
device = torch.device("hpu:0")
torch.hpu.set_device(device)
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
device = torch.device(
"cuda:0" if torch.cuda.is_available() else os.environ.get("ACCELERATE_TORCH_DEVICE", "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.cuda.device_count()
if device.type == "cuda":
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.
if not hasattr(self, "_n_gpu"):
_ = 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_xla_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.distributed_state is not None and self.distributed_state.distributed_type != DistributedType.NO:
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 self.distributed_state is not None:
return self.distributed_state.num_processes
elif is_sagemaker_mp_enabled():
return smp.dp_size() if not smp.state.cfg.prescaled_batch else smp.rdp_size()
return 1
@property
def process_index(self):
"""
The index of the current process used.
"""
requires_backends(self, ["torch"])
if self.distributed_state is not None:
return self.distributed_state.process_index
elif is_sagemaker_mp_enabled():
return smp.dp_rank() if not smp.state.cfg.prescaled_batch else smp.rdp_rank()
return 0
@property
def local_process_index(self):
"""
The index of the local process used.
"""
requires_backends(self, ["torch"])
if self.distributed_state is not None:
return self.distributed_state.local_process_index
elif is_sagemaker_mp_enabled():
return smp.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 local process" if local else "main process"
if self.distributed_state is not None:
is_main_process = (
self.distributed_state.is_local_main_process if local else self.distributed_state.is_main_process
)
elif is_sagemaker_mp_enabled():
is_main_process = smp.rank() == 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_xla_available():
xm.rendezvous(desc)
else:
dist.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_xla_available():
xm.rendezvous(desc)
else:
dist.barrier()
else:
yield
def get_warmup_steps(self, num_training_steps: int):
"""
Get number of steps used for a linear warmup.
"""
warmup_steps = (
int(self.warmup_steps) if self.warmup_steps >= 1 else math.ceil(num_training_steps * self.warmup_steps)
)
return warmup_steps
def _dict_dtype_to_str(self, d: dict[str, Any]) -> None:
"""
Checks whether the passed dictionary and its nested dicts have a *dtype* key and if it's not None,
converts torch.dtype to a string of just the type. For example, `torch.float32` get converted into *"float32"*
string, which can then be stored in the json format.
"""
if d.get("dtype") is not None and not isinstance(d["dtype"], str):
d["dtype"] = str(d["dtype"]).split(".")[1]
for value in d.values():
if isinstance(value, dict):
self._dict_dtype_to_str(value)
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()}>"
# Handle the accelerator_config if passed
if is_accelerate_available() and isinstance(v, AcceleratorConfig):
d[k] = v.to_dict()
# Handle the quantization_config if passed
if k == "model_init_kwargs" and isinstance(v, dict) and "quantization_config" in v:
quantization_config = v.get("quantization_config")
if quantization_config and not isinstance(quantization_config, dict):
d[k]["quantization_config"] = quantization_config.to_dict()
if k == "parallelism_config" and v is not None:
d[k] = v.to_json()
self._dict_dtype_to_str(d)
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's 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`.
For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
`max_steps` is reached.
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: str | IntervalStrategy = "no",
steps: int = 500,
batch_size: int = 8,
accumulation_steps: int | None = None,
delay: float | None = None,
loss_only: bool = False,
):
"""
A method that regroups all arguments linked to 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
eval_strategy.
loss_only (`bool`, *optional*, defaults to `False`):
Ignores all outputs except the loss.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_evaluate(strategy="steps", steps=100)
>>> args.eval_steps
100
```
"""
self.eval_strategy = IntervalStrategy(strategy)
if self.eval_strategy == IntervalStrategy.STEPS and steps == 0:
raise ValueError("Setting `strategy` as 'steps' requires a positive value for `steps`.")
self.do_eval = self.eval_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
return self
def set_testing(
self,
batch_size: int = 8,
loss_only: 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.
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
return self
def set_save(
self,
strategy: str | IntervalStrategy = "steps",
steps: int = 500,
total_limit: int | None = None,
on_each_node: bool = False,
):
"""
A method that regroups all arguments linked to checkpoint saving.
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 = SaveStrategy(strategy)
if self.save_strategy == SaveStrategy.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: str | IntervalStrategy = "steps",
steps: int = 500,
report_to: 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 logging.
Args:
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`.
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"`,
`"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`,
`"swanlab"`, `"tensorboard"`, `"trackio"` 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: str | HubStrategy = "every_save",
token: str | None = None,
private_repo: bool | None = None,
always_push: bool = False,
revision: str | None = None,
):
"""
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 your `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 processing_class e.g. 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 processing_class e.g. 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 `hf auth login`.
private_repo (`bool`, *optional*, defaults to `False`):
Whether to make the repo private. If `None` (default), the repo will be public unless the organization's default is private. This value is ignored if the repo already exists.
always_push (`bool`, *optional*, defaults to `False`):
Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not
finished.
revision (`str`, *optional*):
The revision to use when pushing to the Hub. Can be a branch name, a tag, or a commit hash.
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
self.hub_always_push = always_push
self.hub_revision = revision
return self
def set_optimizer(
self,
name: str | OptimizerNames = "adamw_torch",
learning_rate: float = 5e-5,
weight_decay: float = 0,
beta1: float = 0.9,
beta2: float = 0.999,
epsilon: float = 1e-8,
args: str | None = 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_torch"`):
The optimizer to use: `"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: str | SchedulerType = "linear",
num_epochs: float = 3.0,
max_steps: int = -1,
warmup_steps: float = 0,
warmup_ratio: float | None = None,
):
"""
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`.
For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
`max_steps` is reached.
warmup_steps (`float`, *optional*, defaults to 0):
Number of steps used for a linear warmup from 0 to `learning_rate`. Should be an integer or a float in range `[0,1)`.
If smaller than 1, will be interpreted as ratio of steps used for a linear warmup from 0 to `learning_rate`.
Example:
```py
>>> from transformers import TrainingArguments
>>> args = TrainingArguments("working_dir")
>>> args = args.set_lr_scheduler(name="cosine", warmup_steps=0.05)
>>> args.warmup_steps
0.05
```
"""
if warmup_ratio is not None:
logger.warning("warmup_ratio is deprecated and will be removed in v5.2 . Use `warmup_steps` instead.")
warmup_steps = warmup_ratio
self.lr_scheduler_type = SchedulerType(name)
self.num_train_epochs = num_epochs
self.max_steps = max_steps
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,
persistent_workers: bool = False,
prefetch_factor: int | None = None,
auto_find_batch_size: bool = False,
ignore_data_skip: bool = False,
sampler_seed: int | None = 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`.
persistent_workers (`bool`, *optional*, defaults to `False`):
If True, the data loader will not shut down the worker processes after a dataset has been consumed
once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training,
but will increase RAM usage. Will default to `False`.
prefetch_factor (`int`, *optional*):
Number of batches loaded in advance by each worker.
2 means there will be a total of 2 * num_workers batches prefetched across all workers.
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.dataloader_persistent_workers = persistent_workers
self.dataloader_prefetch_factor = prefetch_factor
self.auto_find_batch_size = auto_find_batch_size
self.ignore_data_skip = ignore_data_skip
self.data_seed = sampler_seed
return self
def _process_fsdp_args(self):
if not self.fsdp:
self.fsdp = []
elif self.fsdp is True:
self.fsdp = [FSDPOption.FULL_SHARD]
elif 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.gradient_checkpointing and (
FSDPOption.FULL_SHARD in self.fsdp or FSDPOption.HYBRID_SHARD in self.fsdp
):
logger.warning(
"When using FSDP full shard, instead of using `gradient_checkpointing` in TrainingArguments, please"
" use `activation_checkpointing` in `fsdp_config`. The former introduces a redundant AllGather"
" operation in backward pass. Reference: https://github.com/huggingface/transformers/issues/30404"
)
if self.fsdp_config is None:
self.fsdp_config = {}
if isinstance(self.fsdp_config, str):
if len(self.fsdp) == 0:
warnings.warn("`--fsdp_config` is useful only when `--fsdp` is specified.")
with open(self.fsdp_config, encoding="utf-8") as f:
self.fsdp_config = json.load(f)
if self.fsdp_config is not None and isinstance(self.fsdp_config, dict):
for k in list(self.fsdp_config.keys()):
if k.startswith("fsdp_"):
v = self.fsdp_config.pop(k)
self.fsdp_config[k[5:]] = v
self.fsdp_config["min_num_params"] = self.fsdp_config.get("min_num_params", 0)
# if fsdp_config["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("transformer_layer_cls_to_wrap", None), str):
self.fsdp_config["transformer_layer_cls_to_wrap"] = [self.fsdp_config["transformer_layer_cls_to_wrap"]]
if len(self.fsdp) == 0 and self.fsdp_config["min_num_params"] > 0:
warnings.warn("`min_num_params` is useful only when `--fsdp` is specified.")
if len(self.fsdp) == 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
warnings.warn("`transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")
if (
len(self.fsdp) > 0
and self.fsdp_config["min_num_params"] > 0
and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None
):
raise ValueError("`min_num_params` and `transformer_layer_cls_to_wrap` are mutually exclusive.")
self.fsdp_config["xla"] = self.fsdp_config.get("xla", False)
self.fsdp_config["xla_fsdp_v2"] = self.fsdp_config.get("xla_fsdp_v2", 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
# Copy the config to avoid modifying the original config (which may be used for JSON serialization)
self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {}).copy()
# 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.")
# accelerate integration for FSDP
fsdp_plugin_args = None
if len(self.fsdp) > 0 and not self.fsdp_config["xla"]:
from accelerate.utils.constants import (
FSDP_AUTO_WRAP_POLICY,
FSDP_SHARDING_STRATEGY,
)
fsdp_plugin_args = {}
for fsdp_option in self.fsdp:
if fsdp_option.upper() in FSDP_SHARDING_STRATEGY:
fsdp_plugin_args["sharding_strategy"] = fsdp_option
elif fsdp_option == FSDPOption.OFFLOAD:
fsdp_plugin_args["cpu_offload"] = True
elif fsdp_option == FSDPOption.AUTO_WRAP:
fsdp_plugin_args["auto_wrap_policy"] = FSDP_AUTO_WRAP_POLICY[0]
if self.fsdp_config["min_num_params"] > 0:
fsdp_plugin_args["min_num_params"] = self.fsdp_config["min_num_params"]
fsdp_plugin_args["auto_wrap_policy"] = FSDP_AUTO_WRAP_POLICY[1]
elif self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
fsdp_plugin_args["transformer_cls_names_to_wrap"] = ",".join(
self.fsdp_config["transformer_layer_cls_to_wrap"]
)
fsdp_version = int(self.fsdp_config.get("version", 1))
fsdp_plugin_args["fsdp_version"] = fsdp_version
prefetch_policy = self.fsdp_config.get("backward_prefetch", "NO_PREFETCH")
if fsdp_version == 2:
fsdp_plugin_args["reshard_after_forward"] = str_to_bool(
str(self.fsdp_config.get("reshard_after_forward", "false")).lower()
)
else:
fsdp_plugin_args["forward_prefetch"] = str_to_bool(
str(self.fsdp_config.get("forward_prefetch", "false")).lower()
)
fsdp_plugin_args["backward_prefetch"] = prefetch_policy.upper()
fsdp_plugin_args["reshard_after_forward"] = str(
self.fsdp_config.get("reshard_after_forward", "FULL_SHARD")
).lower()
fsdp_plugin_args["use_orig_params"] = str_to_bool(
str(self.fsdp_config.get("use_orig_params", "true")).lower()
)
sync_module_states = str(self.fsdp_config.get("sync_module_states", "true")).lower()
cpu_ram_efficient_loading = str(self.fsdp_config.get("cpu_ram_efficient_loading", "false")).lower()
if sync_module_states == "false" and cpu_ram_efficient_loading == "true":
# In this case, all the processes except the main process would have random weights leading
# to unexpected behaviour during training, thus throwing error here to prevent it.
raise ValueError('`sync_module_states` must be `"True"` if `cpu_ram_efficient_loading` is `"True"`')
# we need to set the env here as otherwise we get a warning in accelerate + we need to set it for transformers
fsdp_plugin_args["cpu_ram_efficient_loading"] = str_to_bool(cpu_ram_efficient_loading)
os.environ["FSDP_CPU_RAM_EFFICIENT_LOADING"] = cpu_ram_efficient_loading
fsdp_plugin_args["sync_module_states"] = str_to_bool(sync_module_states)
return fsdp_plugin_args
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"
def str_to_bool(value, to_bool: bool = True) -> int | bool:
"""
Converts a string representation of truth to `True` (1) or `False` (0).
True values are `y`, `yes`, `t`, `true`, `on`, and `1`; False value are `n`, `no`, `f`, `false`, `off`, and `0`;
"""
value = value.lower()
if value in ("y", "yes", "t", "true", "on", "1"):
return 1 if not to_bool else True
elif value in ("n", "no", "f", "false", "off", "0"):
return 0 if not to_bool else False
else:
raise ValueError(f"invalid truth value {value}")
Reference in new issue View Git Blame Copy Permalink