[QuantizationModifier] NVFP4 bugfix -- fused layer update on all modules (#1869)

SUMMARY:
#1772 introduced a bug when running NVFP4 quantization schemes. The call
to `update_fused_layer_weight_global_scales` needs to be run on
Attention and MLP layers, which are not included in `targets` consisting
of quantizable layers inside Attention/MLP. This PR fixes that by
running `update_fused_layer_weight_global_scales` on every module
instead of the targeted ones, which is ok because the call is idempotent
and will only modify if the modules have NVFP4 schemes. This is only a
problem in `QuantizationModifier`, AWQ cannot be used with NVFP4.

TEST PLAN:
Confirmed that the working vs. broken global scales are mismatched
because the update is never run:
```
model.layers.0.self_attn.k_proj.weight_global_scale -- working 9600.0, broken 12992.0
model.layers.0.self_attn.q_proj.weight_global_scale -- working 9600.0, broken 9600.0
model.layers.0.self_attn.v_proj.weight_global_scale -- working 9600.0, broken 12160.0
```

And these changes resolve the regression:
Before
```
vllm (pretrained=/home/dsikka/llm-compressor/examples/quantization_w4a4_fp4/Qwen3-30B-A3B-NVFP4,dtype=auto,max_model_len=4096,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8135|±  |0.0107|
|     |       |strict-match    |     5|exact_match|↑  |0.8097|±  |0.0108|
```
After
```
vllm (pretrained=/home/brian-dellabetta/projects/llm-compressor/Qwen3-30B-A3B-NVFP4,dtype=auto,max_model_len=4096,add_bos_token=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8620|±  |0.0095|
|     |       |strict-match    |     5|exact_match|↑  |0.8575|±  |0.0096|
```

---------

Signed-off-by: Brian Dellabetta <[email protected]>

Author: brian-dellabetta

src/llmcompressor/modifiers/quantization/quantization/base.py

@@ -76,11 +76,18 @@ def on_start(self, state: State, event: Event, **kwargs):
         # TODO: this step can be combined with update_weight_zp_scale
         # once update_fused_layer_weight_global_scales is removed
         # and not required by vLLM
-        for _, module in tqdm.tqdm(named_modules):
+        for _, module in tqdm.tqdm(named_modules, desc="Updating global scales"):
             update_weight_global_scale(module)
 
-        for _, module in tqdm.tqdm(named_modules, desc="Calibrating weights"):
+        # NOTE: update_fused_layer_weight_global_scales operates on Attention
+        # and MLP layers, not quantizable Linear layers. Rather than running
+        # on targeted modules, we need to run on all modules.
+        # Because this call is idempotent, setting all global_scales to the
+        # min value, it is ok to run potentially multiple times for all modules
+        for module in tqdm.tqdm(state.model.modules(), desc="Fusing global scales"):
             update_fused_layer_weight_global_scales(module)
+
+        for _, module in tqdm.tqdm(named_modules, desc="Calibrating weights"):
             update_weight_zp_scale(module)
 
     def on_event(self, state: State, event: Event, **kwargs):