phase19: MoE support — gpt-oss-20b end-to-end inference with TP=2

Add Mixture-of-Experts support for the gpt-oss-20b model (20.9B params,
32 experts × top-4 routing). Key additions:

- ModelConfig: MoE fields (num_local_experts, layer_types, sliding_window,
  attention_bias, explicit head_dim, rope_scaling, swiglu_limit)
- YaRN RoPE: RopeCache::new_yarn() with correct frequency interpolation
  and attention_scaling = 0.1*ln(factor)+1
- Custom GLU kernel: gpt_oss_glu_bf16 (clamped sigmoid gate activation)
- Paged attention with sinks + sliding window kernel variant
- GptOss model struct with expert-parallel TP (split 32 experts across ranks)
- bench-gpt-oss binary for TP inference benchmarking

Verified on dash5 with 2x RTX 5090: 63.6 tok/s decode, ~160ms TTFT.
Model generates topically-coherent output (needs chat template for quality).

Known issues:
- Custom GEMV kernel produces NaN with small N (workaround: pad to M=2)
- Prefill doesn't use attention sinks (uses standard flash attention)
- Output quality requires chat template formatting

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

crates/xserv-model/src/config.rs

@@ -1,6 +1,15 @@
 use serde::Deserialize;
 use std::path::Path;
 
+#[derive(Debug, Clone, Deserialize)]
+pub struct RopeScaling {
+    pub rope_type: Option<String>,
+    pub factor: Option<f64>,
+    pub original_max_position_embeddings: Option<usize>,
+    pub beta_fast: Option<f64>,
+    pub beta_slow: Option<f64>,
+}
+
 #[derive(Debug, Clone, Deserialize)]
 pub struct ModelConfig {
     pub architectures: Option<Vec<String>>,
@@ -46,6 +55,24 @@ pub struct ModelConfig {
     pub rope_theta: Option<f64>,
     #[serde(default)]
     pub tie_word_embeddings: Option<bool>,
+
+    // MoE (gpt-oss)
+    #[serde(default)]
+    pub num_local_experts: Option<usize>,
+    #[serde(default)]
+    pub num_experts_per_tok: Option<usize>,
+    #[serde(default)]
+    pub layer_types: Option<Vec<String>>,
+    #[serde(default)]
+    pub sliding_window: Option<usize>,
+    #[serde(default)]
+    pub attention_bias: Option<bool>,
+    #[serde(default, rename = "head_dim")]
+    pub explicit_head_dim: Option<usize>,
+    #[serde(default)]
+    pub rope_scaling: Option<RopeScaling>,
+    #[serde(default)]
+    pub swiglu_limit: Option<f64>,
 }
 
 impl ModelConfig {
@@ -81,7 +108,7 @@ impl ModelConfig {
     }
 
     pub fn head_dim(&self) -> usize {
-        self.hidden() / self.num_heads()
+        self.explicit_head_dim.unwrap_or_else(|| self.hidden() / self.num_heads())
     }
 
     pub fn ln_eps(&self) -> f32 {
@@ -93,4 +120,28 @@ impl ModelConfig {
     pub fn tied_embeddings(&self) -> bool {
         self.tie_word_embeddings.unwrap_or(true)
     }
+
+    pub fn num_experts(&self) -> usize {
+        self.num_local_experts.unwrap_or(0)
+    }
+
+    pub fn experts_per_token(&self) -> usize {
+        self.num_experts_per_tok.unwrap_or(1)
+    }
+
+    pub fn is_moe(&self) -> bool {
+        self.num_local_experts.unwrap_or(0) > 1
+    }
+
+    pub fn is_sliding_layer(&self, layer_idx: usize) -> bool {
+        self.layer_types
+            .as_ref()
+            .and_then(|lt| lt.get(layer_idx))
+            .map(|t| t == "sliding_attention")
+            .unwrap_or(false)
+    }
+
+    pub fn window_size(&self) -> usize {
+        self.sliding_window.unwrap_or(0)
+    }
 }