Add parameter_name support to _nvfp4_inference_linear_transform#3976
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Add parameter_name support to _nvfp4_inference_linear_transform#3976
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Add `parameter_name` keyword-only argument to support FqnToConfig per-parameter quantization. Replace module.weight reads/writes with getattr/setattr, and use _module_extra_repr partial pattern. [ghstack-poisoned]
Add `parameter_name` keyword-only argument to the dynamic quantization branch (step=None) to support FqnToConfig per-parameter quantization. PREPARE and CONVERT branches are left unchanged as they are Linear-specific observer flow. [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/3976
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit c850131 with merge base 22607b6 ( BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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This was referenced Mar 3, 2026
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…ence_linear_transform" Add `parameter_name` keyword-only argument to the dynamic quantization branch (step=None) to support FqnToConfig per-parameter quantization. PREPARE and CONVERT branches are left unchanged as they are Linear-specific observer flow. [ghstack-poisoned]
…sform" Add `parameter_name` keyword-only argument to the dynamic quantization branch (step=None) to support FqnToConfig per-parameter quantization. PREPARE and CONVERT branches are left unchanged as they are Linear-specific observer flow. [ghstack-poisoned]
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module: not user facing
vkuzo
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Mar 5, 2026
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Stack from ghstack (oldest at bottom):
Add
parameter_namekeyword-only argument to the dynamic quantizationbranch (step=None) to support FqnToConfig per-parameter quantization.
PREPARE and CONVERT branches are left unchanged as they are
Linear-specific observer flow.