docs: fix input/target shapes in DPO and ORPO forward docstrings

[Aryan95614]

Summary

The forward docstrings of LigerFusedLinearDPOFunction and LigerFusedLinearORPOFunction describe _input (and DPO's ref_input) as (batch_size * seq_len, hidden_size) and target as (batch_size * seq_len,). The implementation requires (batch_size, seq_len, hidden_size) and (batch_size, seq_len):

• LigerFusedLinearPreferenceBase chunks dim 0 by sequence (torch.chunk(_input[:len_chosen], dim=0)), reduces per-token log-probs over dim 1, and documents these shapes itself (_compute_loss describes input_chunk as (2 * chunk_size, sequence_length, hidden_size)).
• test/chunked_loss/test_dpo_loss.py and test/chunked_loss/test_orpo_loss.py construct 3D inputs (torch.randn(B, T, H, ...)).
• Following the docstrings as written would silently split tokens instead of sequences into chosen/rejected halves.

Docstring-only change; make checkstyle passes; no kernel or convergence impact.

The same wording also appears in cpo_loss.py (lines 62, 64), simpo_loss.py (lines 70, 72), and kto_loss.py (lines 94, 96, 99, including ref_input). Happy to fold those into this PR if you prefer one sweep.

Testing Done

• Hardware Type: n/a (docstring-only)
• run make checkstyle to ensure code style
• run make test to ensure correctness (unaffected)
• run make test-convergence to ensure convergence (unaffected)