Add a backwards linear function to be used with the fused mla q up-proj

[chaseblock]

Description

Please include a brief summary of the changes, relevant motivation and context.

Fixes # (issue)

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

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• Change A
• Change B

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes

[greptile-apps]

Greptile Summary

The PR adds a reusable linear-backward entry point for fused operations and exposes it through the fused MLA Q up-projection helper.

• Constructs LinearBwdArgs for quantized and non-quantized projection backward passes.
• Supports tensor/sequence parallelism, deferred or fused weight-gradient accumulation, and optional gradient computation.
• Delegates the fused MLA Q up-projection backward path to the new helper.

Confidence Score: 4/5

The PR should not merge until the outstanding bias-gradient contract is fixed for callers that enable bias.

The new wrapper allows use_bias=True, and _linear_backward computes the corresponding gradient, but the wrapper discards that result and provides no other path for a fused caller to update the bias.

Files Needing Attention: transformer_engine/pytorch/module/linear.py

Important Files Changed

Filename	Overview
transformer_engine/pytorch/module/linear.py	Adds the shared backward helper, but its optional bias-gradient result remains discarded.
transformer_engine/pytorch/attention/fused_mla_q_uproj.py	Adds a thin classmethod that forwards fused MLA projection-backward inputs and keyword options to the shared helper.

_{Reviews (2): Last reviewed commit: "Remove redundant import in backward_line..." | Re-trigger Greptile}

[greptile-apps]

Bias gradient is discarded

When use_bias=True, _linear_backward computes a bias gradient, but this wrapper discards the third return value and exposes only dgrad and wgrad, preventing the caller from updating the bias parameter.

Knowledge Base Used: PyTorch Fused Modules (transformer_engine/pytorch/module)