Upload testing suite for DistillationTrainer

[cmpatino]

What does this PR do?

Uploads tests for the DistillationTrainer

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Note

Low Risk
Test-only change; no production code paths are modified, with risk limited to potential CI flakiness due to model/dataset dependencies and optional accelerator coverage.

Overview
Adds a new experimental test module covering DistillationTrainer and related utilities.

The suite validates DistillationConfig argument constraints (teacher server vs liger, server URL required, reverse-KL mode restrictions), correctness of build_teacher_request_inputs trimming/prompt-length logic, and generalized_jsd_loss behavior across beta, temperature, and reductions. It also adds integration-style tests that run a short train with a local teacher (including checkpoint output), optionally exercises the liger path, verifies parity between local-teacher and mocked vLLM-teacher-server losses in sampled top-1 mode, and checks _RepeatBatchDataLoader forwards set_epoch.

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[qgallouedec]

Thanks for adding tests to DistillationTrainer, it had none, so this is directionally welcome. A few things worth addressing before merge:

1. Heavy use of DistillationTrainer.__new__(...) + manual attribute assignment, plus five Dummy*/custom mock classes. Every test assembles a fake trainer by bypassing __init__ and setting ~10 private attributes inline. This couples every test to internal attribute names. Any rename or new attribute used in compute_loss silently breaks the suite. It also cuts against two principles: consistency (the rest of tests/experimental/, e.g. test_gkd_trainer.py, loads a tiny real model and exercises the real __init__) and simplicity (the mock scaffolding and duplicated attribute setup in test_server_teacher_path_handles_variable_prompt_lengths / ..._padded_completions is exactly that). A tiny-model fixture would remove most of it.

2. No end-to-end test. One short trainer.train() on a tiny student+teacher would catch far more than the current mocked suite combined, and would make most of the attribute-juggling tests redundant. Plus it would be more align with the principle of testing behavior over implementation.

[qgallouedec]

this is tautological. The expected value is computed by calling the same private helper _jsd_divergence with the same support/mask construction that compute_loss uses internally, so it tests wiring, not semantics. Any bug inside _jsd_divergence is invisible. Derive the expected value from first principles (plain JSD formula) or drop it.

[qgallouedec]

fragile: any unrelated DeprecationWarning from a dependency will break it. The pytest.raises is already the real assertion

[cmpatino]

Thank you for the feedback! I addressed your comments with the following changes:

• Followed GKD's tests and used a small model and dataset to test the trainer.
• Improved the Liger tests by following the testing approach from the SFTTrainer.

[HuggingFaceDocBuilderDev]

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