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[docs] Trainer #27986

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[docs] Trainer #27986

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2 changes: 2 additions & 0 deletions docs/source/en/_toctree.yml
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Expand Up @@ -135,6 +135,8 @@
title: Overview
- local: quantization
title: Quantization
- local: trainer
title: Trainer
- sections:
- local: perf_train_gpu_one
title: Methods and tools for efficient training on a single GPU
Expand Down
335 changes: 6 additions & 329 deletions docs/source/en/main_classes/trainer.md
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# Trainer

The [`Trainer`] class provides an API for feature-complete training in PyTorch for most standard use cases. It's used in most of the [example scripts](https://github.com/huggingface/transformers/tree/main/examples).
The [`Trainer`] class provides an API for feature-complete training in PyTorch, and it supports distributed training on multiple GPUs/TPUs, mixed precision for [NVIDIA GPUs](https://nvidia.github.io/apex/), [AMD GPUs](https://rocm.docs.amd.com/en/latest/rocm.html), and [`torch.amp`](https://pytorch.org/docs/stable/amp.html) for PyTorch. [`Trainer`] goes hand-in-hand with the [`TrainingArguments`] class, which offers a wide range of options to customize how a model is trained. Together, these two classes provide a complete training API.

<Tip>

If you're looking to fine-tune a language model like Llama-2 or Mistral on a text dataset using autoregressive techniques, consider using [`trl`](https://github.com/huggingface/trl)'s [`~trl.SFTTrainer`]. The [`~trl.SFTTrainer`] wraps the [`Trainer`] and is specially optimized for this particular task and supports sequence packing, LoRA, quantization, and DeepSpeed for efficient scaling to any model size. On the other hand, the [`Trainer`] is a more versatile option, suitable for a broader spectrum of tasks.

</Tip>

Before instantiating your [`Trainer`], create a [`TrainingArguments`] to access all the points of customization during training.

The API supports distributed training on multiple GPUs/TPUs, mixed precision through [NVIDIA Apex] for NVIDIA GPUs, [ROCm APEX](https://github.com/ROCmSoftwarePlatform/apex) for AMD GPUs, and Native AMP for PyTorch.

The [`Trainer`] contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:

- **get_train_dataloader** -- Creates the training DataLoader.
- **get_eval_dataloader** -- Creates the evaluation DataLoader.
- **get_test_dataloader** -- Creates the test DataLoader.
- **log** -- Logs information on the various objects watching training.
- **create_optimizer_and_scheduler** -- Sets up the optimizer and learning rate scheduler if they were not passed at
init. Note, that you can also subclass or override the `create_optimizer` and `create_scheduler` methods
separately.
- **create_optimizer** -- Sets up the optimizer if it wasn't passed at init.
- **create_scheduler** -- Sets up the learning rate scheduler if it wasn't passed at init.
- **compute_loss** - Computes the loss on a batch of training inputs.
- **training_step** -- Performs a training step.
- **prediction_step** -- Performs an evaluation/test step.
- **evaluate** -- Runs an evaluation loop and returns metrics.
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
[`Seq2SeqTrainer`] and [`Seq2SeqTrainingArguments`] inherit from the [`Trainer`] and [`TrainingArgument`] classes and they're adapted for training models for sequence-to-sequence tasks such as summarization or translation.

<Tip warning={true}>

The [`Trainer`] class is optimized for 🤗 Transformers models and can have surprising behaviors
when you use it on other models. When using it on your own model, make sure:
when used with other models. When using it with your own model, make sure:

- your model always return tuples or subclasses of [`~utils.ModelOutput`].
- your model always return tuples or subclasses of [`~utils.ModelOutput`]
- your model can compute the loss if a `labels` argument is provided and that loss is returned as the first
element of the tuple (if your model returns tuples)
- your model can accept multiple label arguments (use the `label_names` in your [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`.
- your model can accept multiple label arguments (use `label_names` in [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`

</Tip>

Here is an example of how to customize [`Trainer`] to use a weighted loss (useful when you have an unbalanced training set):

```python
from torch import nn
from transformers import Trainer


class CustomTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
labels = inputs.pop("labels")
# forward pass
outputs = model(**inputs)
logits = outputs.get("logits")
# compute custom loss (suppose one has 3 labels with different weights)
loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
return (loss, outputs) if return_outputs else loss
```

Another way to customize the training loop behavior for the PyTorch [`Trainer`] is to use [callbacks](callback) that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early stopping).


## Trainer
## Trainer[[api-reference]]

[[autodoc]] Trainer
- all
Expand All @@ -100,105 +53,6 @@ Another way to customize the training loop behavior for the PyTorch [`Trainer`]
[[autodoc]] Seq2SeqTrainingArguments
- all

## Checkpoints

By default, [`Trainer`] will save all checkpoints in the `output_dir` you set in the
[`TrainingArguments`] you are using. Those will go in subfolder named `checkpoint-xxx` with xxx
being the step at which the training was at.

Resuming training from a checkpoint can be done when calling [`Trainer.train`] with either:

- `resume_from_checkpoint=True` which will resume training from the latest checkpoint
- `resume_from_checkpoint=checkpoint_dir` which will resume training from the specific checkpoint in the directory
passed.

In addition, you can easily save your checkpoints on the Model Hub when using `push_to_hub=True`. By default, all
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
the `hub-strategy` value of your [`TrainingArguments`] to either:

- `"checkpoint"`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
resume training easily with `trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")`.
- `"all_checkpoints"`: all checkpoints are pushed like they appear in the output folder (so you will get one
checkpoint folder per folder in your final repository)


## Logging

By default [`Trainer`] will use `logging.INFO` for the main process and `logging.WARNING` for the replicas if any.

These defaults can be overridden to use any of the 5 `logging` levels with [`TrainingArguments`]'s
arguments:

- `log_level` - for the main process
- `log_level_replica` - for the replicas

Further, if [`TrainingArguments`]'s `log_on_each_node` is set to `False` only the main node will
use the log level settings for its main process, all other nodes will use the log level settings for replicas.

Note that [`Trainer`] is going to set `transformers`'s log level separately for each node in its
[`Trainer.__init__`]. So you may want to set this sooner (see the next example) if you tap into other
`transformers` functionality before creating the [`Trainer`] object.

Here is an example of how this can be used in an application:

```python
[...]
logger = logging.getLogger(__name__)

# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)

# set the main code and the modules it uses to the same log-level according to the node
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)

trainer = Trainer(...)
```

And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
warnings you could run it as:

```bash
my_app.py ... --log_level warning --log_level_replica error
```

In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to
change the above to:

```bash
my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0
```

and then only the main process of the first node will log at the "warning" level, and all other processes on the main
node and all processes on other nodes will log at the "error" level.

If you need your application to be as quiet as possible you could do:

```bash
my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0
```

(add `--log_on_each_node 0` if on multi-node environment)


## Randomness

When resuming from a checkpoint generated by [`Trainer`] all efforts are made to restore the
_python_, _numpy_ and _pytorch_ RNG states to the same states as they were at the moment of saving that checkpoint,
which should make the "stop and resume" style of training as close as possible to non-stop training.

However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
determinism please refer to [Controlling sources of randomness](https://pytorch.org/docs/stable/notes/randomness). As explained in the document, that some of those settings
that make things deterministic (.e.g., `torch.backends.cudnn.deterministic`) may slow things down, therefore this
can't be done by default, but you can enable those yourself if needed.


## Specific GPUs Selection

Let's discuss how you can tell your program which GPUs are to be used and in what order.
Expand Down Expand Up @@ -295,9 +149,6 @@ In this example we are working with just 2 GPUs, but of course the same would ap

Also if you do set this environment variable it's the best to set it in your `~/.bashrc` file or some other startup config file and forget about it.




## Trainer Integrations

The [`Trainer`] has been extended to support libraries that may dramatically improve your training
Expand Down Expand Up @@ -579,156 +430,6 @@ Finally, please, remember that, 🤗 `Trainer` only integrates MPS backend, ther
have any problems or questions with regards to MPS backend usage, please,
file an issue with [PyTorch GitHub](https://github.com/pytorch/pytorch/issues).


## Using Accelerate Launcher with Trainer

Accelerate now powers Trainer. In terms of what users should expect:
- They can keep using the Trainer ingterations such as FSDP, DeepSpeed vis trainer arguments without any changes on their part.
- They can now use Accelerate Launcher with Trainer (recommended).

Steps to use Accelerate Launcher with Trainer:
1. Make sure 🤗 Accelerate is installed, you can't use the `Trainer` without it anyway. If not `pip install accelerate`. You may also need to update your version of Accelerate: `pip install accelerate --upgrade`
2. Run `accelerate config` and fill the questionnaire. Below are example accelerate configs:
a. DDP Multi-node Multi-GPU config:
```yaml
compute_environment: LOCAL_MACHINE
distributed_type: MULTI_GPU
downcast_bf16: 'no'
gpu_ids: all
machine_rank: 0 #change rank as per the node
main_process_ip: 192.168.20.1
main_process_port: 9898
main_training_function: main
mixed_precision: fp16
num_machines: 2
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```

b. FSDP config:
```yaml
compute_environment: LOCAL_MACHINE
distributed_type: FSDP
downcast_bf16: 'no'
fsdp_config:
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_backward_prefetch_policy: BACKWARD_PRE
fsdp_forward_prefetch: true
fsdp_offload_params: false
fsdp_sharding_strategy: 1
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sync_module_states: true
fsdp_transformer_layer_cls_to_wrap: BertLayer
fsdp_use_orig_params: true
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 2
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```
c. DeepSpeed config pointing to a file:
```yaml
compute_environment: LOCAL_MACHINE
deepspeed_config:
deepspeed_config_file: /home/user/configs/ds_zero3_config.json
zero3_init_flag: true
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```

d. DeepSpeed config using accelerate plugin:
```yaml
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 1
gradient_clipping: 0.7
offload_optimizer_device: cpu
offload_param_device: cpu
zero3_init_flag: true
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: bf16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
```

3. Run the Trainer script with args other than the ones handled above by accelerate config or launcher args.
Below is an example to run `run_glue.py` using `accelerate launcher` with FSDP config from above.

```bash
cd transformers

accelerate launch \
./examples/pytorch/text-classification/run_glue.py \
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir
```

4. You can also directly use the cmd args for `accelerate launch`. Above example would map to:

```bash
cd transformers

accelerate launch --num_processes=2 \
--use_fsdp \
--mixed_precision=bf16 \
--fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP \
--fsdp_transformer_layer_cls_to_wrap="BertLayer" \
--fsdp_sharding_strategy=1 \
--fsdp_state_dict_type=FULL_STATE_DICT \
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--max_seq_length 128 \
--per_device_train_batch_size 16 \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir
```

For more information, please refer the 🤗 Accelerate CLI guide: [Launching your 🤗 Accelerate scripts](https://huggingface.co/docs/accelerate/basic_tutorials/launch).

Sections that were moved:

[ <a href="./deepspeed#deepspeed-trainer-integration">DeepSpeed</a><a id="deepspeed"></a>
Expand All @@ -755,27 +456,3 @@ Sections that were moved:
| <a href="./deepspeed#deepspeed-grad-clip">Gradient Clipping</a><a id="gradient-clipping"></a>
| <a href="./deepspeed#deepspeed-weight-extraction">Getting The Model Weights Out</a><a id="getting-the-model-weights-out"></a>
]

## Boost your fine-tuning performances using NEFTune


NEFTune is a technique to boost the performance of chat models and was introduced by the paper “NEFTune: Noisy Embeddings Improve Instruction Finetuning” from Jain et al. it consists of adding noise to the embedding vectors during training. According to the abstract of the paper:

> Standard finetuning of LLaMA-2-7B using Alpaca achieves 29.79% on AlpacaEval, which rises to 64.69% using noisy embeddings. NEFTune also improves over strong baselines on modern instruction datasets. Models trained with Evol-Instruct see a 10% improvement, with ShareGPT an 8% improvement, and with OpenPlatypus an 8% improvement. Even powerful models further refined with RLHF such as LLaMA-2-Chat benefit from additional training with NEFTune.

<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/neft-screenshot.png">
</div>

To use it in `Trainer` simply pass `neftune_noise_alpha` when creating your `TrainingArguments` instance. Note that to avoid any surprising behaviour, NEFTune is disabled after training to retrieve back the original behaviour of the embedding layer.

```python
from transformers import Trainer, TrainingArguments

args = TrainingArguments(..., neftune_noise_alpha=0.1)
trainer = Trainer(..., args=args)

...

trainer.train()
```
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