Summary: This PR simplifies the HF knowledge distillation trainer by removing mtd.convert() class-swap in favor of explicit teacher forwarding, enhances ModelOptHFTrainer with Liger fused loss, per-parameter LRs, parameter freezing, and refactors the llm_qat example to use YAML configs with a new ModelOptArgParser.

Issues Found:

1. [Correctness] CRITICAL: recipe.ptq_cfg does not exist — should be recipe.quantize
ModelOptPTQRecipe (in modelopt/recipe/config.py:69) exposes its quant config via the quantize attribute, not ptq_cfg. This will raise AttributeError at runtime in two places:

   • examples/llm_qat/simple_qat_train.py:126 — model = mtq.quantize(model, recipe.ptq_cfg, calibrate)
   • modelopt/torch/quantization/plugins/transformers_trainer.py:217 — return recipe.ptq_cfg

   The correct usage is already in examples/llm_qat/quantize.py:77 (ptq_cfg = recipe.quantize), confirming this is a copy-paste error. Existing usage in examples/llm_ptq/hf_ptq.py also uses recipe.quantize.

2. [Correctness] LMLogitsLoss.forward double-sums — returns scalar instead of per-token losses
   LogitsDistillationLoss.forward with reduction="none" already sums over the vocab dimension (line 64 of losses.py), returning shape (B*S,). The new LMLogitsLoss.forward then does another .sum(dim=-1) on this 1D tensor, collapsing it to a scalar. This makes the ignore-index masking in _standard_kd_loss a no-op — padding tokens contribute equally to the loss.

3. [Correctness] Inconsistent causal shift between standard and Liger KD paths
   _liger_kd_loss applies the standard causal LM shift (hidden_states[..., :-1, :], labels[..., 1:]) before computing JSD. But _standard_kd_loss applies no shift — it computes KL-div on all B*S positions and masks with unshifted labels. While comparing student/teacher at the same position is valid, the different masking alignment means the two paths produce semantically different losses for the same input. This will be surprising when toggling --use_liger_kernel.

4. [Correctness] _forward_redirect doesn't restore module.forward on failure
   If the module(dummy) call raises before entering wrapped_forward (e.g., FSDP pre-forward hook fails), module.forward remains patched to wrapped_forward. A try/finally would be safer.

5. [Tests] Low coverage on core library files
   Codecov reports ~20% patch coverage for transformers.py (165 missing lines) and huggingface.py (82 missing lines). The new Liger fused loss path, _forward_redirect, _sharded_liger_compute, parameter freezing, and save_dtype rewriting have no unit test coverage. These are critical code paths for distributed training correctness.

6. [Correctness] save_dtype defaults to "bfloat16" instead of preserving original model dtype
   The old QATTrainer saved the model's original dtype (self._original_dtype). The new ModelOptHFTrainer.save_model hardcodes save_dtype="bfloat16" by default. For models originally in float16, this silently changes the config.json dtype, which may affect downstream inference engines.

7. [Readability] Misleading shape comments in LMLogitsLoss
   The comment # (B*S, V) on the super().forward() call is wrong — the parent returns (B*S,) when reduction="none". This directly led to bug #2.

Suggestions:

• The liger-kernel>=0.5.0 addition to pyproject.toml [hf] extras makes it a hard install dependency for all HF users. Since usage is guarded by --use_liger_kernel, consider making it an optional extra ([liger] or [hf-liger]) to avoid install issues in constrained environments.
• The pre-commit hook for generate-arguments-md uses language: system and runs python examples/llm_qat/train.py --generate_docs. This requires all modelopt dependencies to be installed in the pre-commit environment, which will fail for most contributors. Consider language: python with explicit dependencies, or making it a manual step.
• _dataset_cache (module-level mutable dict in dataset_utils.py) acts as an in-memory cache but is never evicted. In long-running processes or notebooks, this could hold large datasets in memory indefinitely.

Overall Assessment: The architectural direction is sound — removing mtd.convert() class-swap in favor of explicit teacher forwarding is a significant simplification. The ModelOptArgParser and YAML-based config system is a good developer experience improvement. However, there are two critical correctness bugs (recipe.ptq_cfg and LMLogitsLoss double-sum) that must be fixed before merge, plus the KD loss path inconsistency warrants discussion.