Update excluded modules for Qwen3.5 dense PTQ

[amukkara]

What does this PR do?

Type of change: Bug fix

For Qwen3.5 dense models, in_proj modules in linear attention are to be left unquantized.
Example in Qwen3.5-27B-FP8: https://huggingface.co/Qwen/Qwen3.5-27B-FP8/blob/main/config.json#L148
This PR updates _default_disabled_quantizer_config so that all Qwen3.5 dense models are quantized with the same exclusion pattern.

Usage

bash examples/llm_ptq/scripts/huggingface_example.sh --model Qwen/Qwen3.5-4B  --quant fp8 --tasks quant

Testing

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Additional Information

Summary by CodeRabbit

• Bug Fixes
  • Updated default quantization settings to explicitly disable quantization for certain linear attention projection components, preventing unintended quantization and improving model accuracy and stability.

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• modelopt/torch/quantization/config.py

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• modelopt/torch/quantization/config.py

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📝 Walkthrough

Walkthrough

Two deny-list entries were appended to the default disabled quantizer configuration to explicitly disable quantizers matching *linear_attn.in_proj_a* and *linear_attn.in_proj_b*.

Changes

Cohort / File(s)	Summary
Quantization Configuration modelopt/torch/quantization/config.py	Appended two entries to _default_disabled_quantizer_cfg to set enable: False for quantizer name patterns *linear_attn.in_proj_a* and *linear_attn.in_proj_b*.

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~2 minutes

🚥 Pre-merge checks | ✅ 6

✅ Passed checks (6 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title accurately describes the main change: updating excluded modules for Qwen3.5 dense PTQ by disabling quantizers for linear_attn.in_proj_a/b modules.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Security Anti-Patterns	✅ Passed	PR adds minimal quantizer configuration exclusions with no security-sensitive patterns, unsafe deserialization, eval/exec, or new dependencies.

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🧹 Nitpick comments (1)

modelopt/torch/quantization/config.py (1)

231-232: Add a focused regression test for these new exclusion patterns.

Line 231-232 update global default exclusions; please add a test that verifies quantizers matching *linear_attn.in_proj_a* and *linear_attn.in_proj_b* are disabled after config application. This helps lock in the intended Qwen3.5 PTQ behavior.

🤖 Prompt for AI Agents

Verify each finding against the current code and only fix it if needed.

In `@modelopt/torch/quantization/config.py` around lines 231 - 232, Add a unit
test that verifies the new exclusion patterns "*linear_attn.in_proj_a*" and
"*linear_attn.in_proj_b*" actually disable matching quantizers: import the
exclusion patterns from modelopt.torch.quantization.config (e.g.,
DEFAULT_EXCLUSIONS or the global exclusions variable), create mock quantizer
names like "encoder.linear_attn.in_proj_a.weight" and
"decoder.linear_attn.in_proj_b.bias" and then use the module's
exclusion-matching helper (e.g., matches_exclusion, is_excluded, or the function
that decides quantizer enablement) to assert those names are considered
excluded/disabled after applying the config; fail the test if any of those
quantizers remain enabled.

🤖 Prompt for all review comments with AI agents

Verify each finding against the current code and only fix it if needed.

Nitpick comments:
In `@modelopt/torch/quantization/config.py`:
- Around line 231-232: Add a unit test that verifies the new exclusion patterns
"*linear_attn.in_proj_a*" and "*linear_attn.in_proj_b*" actually disable
matching quantizers: import the exclusion patterns from
modelopt.torch.quantization.config (e.g., DEFAULT_EXCLUSIONS or the global
exclusions variable), create mock quantizer names like
"encoder.linear_attn.in_proj_a.weight" and "decoder.linear_attn.in_proj_b.bias"
and then use the module's exclusion-matching helper (e.g., matches_exclusion,
is_excluded, or the function that decides quantizer enablement) to assert those
names are considered excluded/disabled after applying the config; fail the test
if any of those quantizers remain enabled.

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• modelopt/torch/quantization/config.py