xserv-chat: support gpt-oss-20b with TP; fix GEMV precision bug

- Add ChatModel enum dispatching between Qwen3 and GptOss based on
  config.is_moe(), following the TP engine pattern.
- Add --tp N flag for tensor-parallel inference (required for 39GB
  gpt-oss-20b which doesn't fit on a single 32GB GPU).
- Add gpt-oss harmony chat template with channel/message format.
- Replace hardcoded is_stop_token() with tokenizer.is_eos() for
  multi-model EOS support.
- Restore gpt-oss hardcoded prompt template in server api.rs, lost
  during the Jinja template refactor.
- Fix GEMV race condition: the K-split kernel zeroed the FP32
  accumulator inside the kernel (block k=0) while other blocks
  atomicAdd'd concurrently. Pre-zero with cudaMemsetAsync instead.
- Update benchmark docs with post-fix results.

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

crates/xserv-server/src/api.rs

@@ -169,8 +169,10 @@ fn raise_exception(msg: String) -> Result<String, minijinja::Error> {
 // ---------------------------------------------------------------------------
 
 fn build_prompt_hardcoded(messages: &[Message], model_type: &str) -> String {
+    if model_type == "gpt_oss" {
+        return build_prompt_gpt_oss(messages);
+    }
     // Default: Qwen3 ChatML format
-    let _ = model_type;
     let mut prompt = String::new();
     for msg in messages {
         match msg.role.as_str() {
@@ -189,6 +191,41 @@ fn build_prompt_hardcoded(messages: &[Message], model_type: &str) -> String {
     prompt
 }
 
+fn build_prompt_gpt_oss(messages: &[Message]) -> String {
+    let mut prompt = String::new();
+    prompt.push_str("<|start|>system<|message|>");
+    prompt.push_str("You are a helpful assistant.\n\n# Valid channels: analysis, commentary, final. Channel must be included for every message.");
+    prompt.push_str("<|end|>");
+    let dev_instructions: String = messages
+        .iter()
+        .filter(|m| m.role == "system")
+        .map(|m| m.content.as_str())
+        .collect::<Vec<_>>()
+        .join("\n\n");
+    if !dev_instructions.is_empty() {
+        prompt.push_str("<|start|>developer<|message|># Instructions\n\n");
+        prompt.push_str(&dev_instructions);
+        prompt.push_str("<|end|>");
+    }
+    for msg in messages {
+        match msg.role.as_str() {
+            "user" => {
+                prompt.push_str("<|start|>user<|message|>");
+                prompt.push_str(&msg.content);
+                prompt.push_str("<|end|>");
+            }
+            "assistant" => {
+                prompt.push_str("<|start|>assistant<|channel|>final<|message|>");
+                prompt.push_str(&msg.content);
+                prompt.push_str("<|end|>");
+            }
+            _ => {}
+        }
+    }
+    prompt.push_str("<|start|>assistant<|channel|>final<|message|>");
+    prompt
+}
+
 // ---------------------------------------------------------------------------
 // HTTP handlers
 // ---------------------------------------------------------------------------