Add support for Custom Accelerate Instance in Trainer

[Flakes342]

What does this PR do?

Fixes #40616

This PR adds the ability to pass a custom Accelerator instance to the Trainer constructor, giving users fine-grained control over distributed training configurations while maintaining all existing Trainer functionality.

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  to it if that's the case. Allow users to pass through Accelerate instance #40616
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  here are tips on formatting docstrings.
• Did you write any new necessary tests?

Description

Currently, users who need advanced distributed training configurations (custom DeepSpeed settings, specific FSDP configurations, etc.) have limited options to customize the Accelerator used by Trainer. The existing approach forces users to either modify TrainingArguments which doesn't expose all accelerate options to them or use accelerator_config which is less intuitive and limited.

This change allows users to create and configure their own Accelerator instance with full control over:

• DeepSpeed configurations with custom plugins
• FSDP settings and policies
• Tensor parallelism configurations
• Custom dataloader behaviors
• Advanced gradient accumulation strategies
• Experiment tracking and logging setup

Changes

• Modified Trainer initialization to add an optional accelerator parameter
• Added postprocess_accelerator method: Handles postprocessing for user-provided accelerators - similar to current create_accelerator_and_postprocess method
• Refactored accelerator initialization: Clean separation between creation and postprocessing logic

Example

Adding a simple example of how custom accelerator functionality can be used in trainer:

from accelerate import Accelerator
from accelerate.utils import DeepSpeedPlugin

# Custom DeepSpeed configuration
deepspeed_plugin = DeepSpeedPlugin(
    zero_stage=3,
    offload_optimizer_device="cpu",
    offload_param_device="cpu",
    zero3_init_flag=True,
    zero3_save_16bit_model=True
)

accelerator = Accelerator(deepspeed_plugin=deepspeed_plugin)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    accelerator=accelerator
)

Testing

Added comprehensive tests to trainer/test_trainer.py covering:

• Basic custom accelerator functionality
• Backward compatibility (no accelerator parameter)
• Postprocessing attribute setup
• Settings preservation
• Gather function configuration
• End-to-end training integration

Please let me know if the tests need to be written in some other way. Also, please review and provide your comments and I'd be happy to incorporate any corrections/suggestions.

Who can review? @Rocketknight1, @SunMarc, @anyone :)

[Flakes342]

I wanted to create this as a different method to support any modifications to custom accelerate function in future too.

[Flakes342]

Again, I am not sure what these test failures are..

[Flakes342]

Changes for this PR have been done in:
src/transformers/trainer.py
tests/trainer/tests_trainer.py

Rest all seems to have happened because of auto formatting fix