fix: use checkpoint input_scale for activation global scale (not hardcoded 1/2688)

The checkpoint stores input_scale per projection — the pre-computed
activation normalization factor. Using 1/2688 was wrong for most layers
(e.g. down_proj input_scale=0.031 vs 1/2688=0.000372 — 83x off).
This caused under-quantized activations and garbage output.

vllm/nvfp4_cutedsl.py

@@ -60,8 +60,8 @@ class CuTeDSLMoERunner:
         self._l1_gsb = None
         self._l2_gsb = None
         
-        self._l1_activation_global_scale = 1.0 / 2688.0
-        self._l2_activation_global_scale = 1.0 / 2688.0
+        self._l1_activation_global_scale = None  # set from checkpoint input_scale
+        self._l2_activation_global_scale = None
         
         # Pre-allocated cudagraph buffers (set in _allocate_buffers)
         self._token_indices = None

vllm/patches/deepseek_v4.py

@@ -512,6 +512,15 @@ class DeepseekV4MegaMoEExperts(nn.Module):
         self._cutedsl_runner.l2_sf = l2_sf
         self._cutedsl_runner.l2_gs = l2_gs
 
+        # Set activation global scales from checkpoint input_scale
+        # The input_scale is the pre-computed activation normalization factor
+        # for each projection. Using hardcoded 1/2688 gives wrong scale for most layers.
+        # input_scale shape: (num_experts, 2) for w13 (gate, up), (num_experts, 1) for w2
+        l1_igs = self._w13_input_scale[:, 0] if self._w13_input_scale is not None else None  # gate input_scale
+        l2_igs = self._w2_input_scale[:, 0] if self._w2_input_scale is not None else None  # down input_scale
+        self._cutedsl_runner.l1_activation_global_scale = l1_igs.mean().item() if l1_igs is not None else 1.0 / 2688.0
+        self._cutedsl_runner.l2_activation_global_scale = l2_igs.mean().item() if l2_igs is not None else 1.0 / 2688.0
+
         # Drop the original loader-side parameters
         self._w13_input_scale = self.w13_input_scale.data.clone()
         self._w2_input_scale = self.w2_input_scale.data.clone()