[PyTorch] Avoid saving fp8_tensors in certain scenarios

[cyanguwa]

Description

This PR avoids saving fp8_tensors for cases such as NVTE_FP8_DPA_BWD=0 and fp8_mha=True.

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 refractor

Changes

Please list the changes introduced in this PR:

• Reduces the number of FP8 tensors for certain situations

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

[timmoon10]

Debugging code?

Suggested change

	# elif fp8_meta["recipe"].fp8_mha:
	# fp8_tensors = (
	# None,
	# None,
	# None,
	# None,
	# fp8_meta["scaling_fwd"].scale.clone(),
	# fp8_meta["scaling_fwd"].scale_inv.clone(),
	# )

Also, why does the unfused case have different logic for fp8_meta["recipe"].fp8_mha than the QKV-fused and KV-fused cases?