Log node request processing so operators can see live activity in the console.

packages/node/meshnet_node/relay_bridge.py

@@ -164,6 +164,9 @@ class RelayHttpBridge:
         path = str(payload.get("path") or "/")
         headers = payload.get("headers") if isinstance(payload.get("headers"), dict) else {}
 
+        req_suffix = f" request_id={request_id}" if request_id else ""
+        print(f"  [node] relay {method} {path}{req_suffix}", flush=True)
+
         # body_base64 carries binary data (e.g. bfloat16 activation tensors) safely.
         # Fallback to text "body" for backward-compat with non-binary requests.
         body_b64 = payload.get("body_base64")

packages/node/meshnet_node/torch_server.py

@@ -113,6 +113,10 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
     def log_message(self, fmt, *args):  # noqa: suppress request logs in tests
         pass
 
+    def _request_log_suffix(self) -> str:
+        req_id = self.headers.get("X-Meshnet-Request-Id") or self.headers.get("X-Request-Id")
+        return f" request_id={req_id}" if req_id else ""
+
     def do_POST(self):
         server: _TorchHTTPServer = self.server  # type: ignore[assignment]
         if self.path == "/forward":
@@ -199,8 +203,18 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
             return
 
         server.forward_chunk_count += 1
-        if int(self.headers.get("X-Meshnet-Hop-Index", "0")) > 0:
+        hop_index = int(self.headers.get("X-Meshnet-Hop-Index", "0"))
+        if hop_index > 0:
             server.received_activations = True
+        if chunk_index_value == 0:
+            shard_start = getattr(server.backend, "shard_start", "?")
+            shard_end = getattr(server.backend, "shard_end", "?")
+            print(
+                f"  [node] forward hop={hop_index} "
+                f"layers={shard_start}-{shard_end} "
+                f"session={session[:8]}{self._request_log_suffix()}",
+                flush=True,
+            )
 
         start_layer_header = self.headers.get("X-Meshnet-Start-Layer")
         start_layer = int(start_layer_header) if start_layer_header else None
@@ -311,20 +325,53 @@ class _TorchHandler(http.server.BaseHTTPRequestHandler):
         temperature = float(body.get("temperature") or 1.0)
         top_p = float(body.get("top_p") or 1.0)
 
+        print(
+            f"  [node] processing chat model={model_name!r} stream={stream} "
+            f"max_tokens={max_tokens}{self._request_log_suffix()}",
+            flush=True,
+        )
+
         # Fast path: this node owns the complete model — use HF generate() with KV cache.
         # Avoids the single-token-per-forward-pass limitation of the distributed path.
         if backend.is_head and backend.is_tail:
+            gen_started = time.monotonic()
             try:
                 if stream:
-                    self._stream_openai_response(
-                        backend.generate_text_streaming(messages, max_tokens, temperature, top_p),
-                        model_name,
+                    token_count = 0
+
+                    def _counting_stream():
+                        nonlocal token_count
+                        for token_text in backend.generate_text_streaming(
+                            messages, max_tokens, temperature, top_p,
+                        ):
+                            if token_text:
+                                token_count += 1
+                            yield token_text
+
+                    self._stream_openai_response(_counting_stream(), model_name)
+                    print(
+                        f"  [node] chat complete (stream) tokens={token_count} "
+                        f"elapsed_s={time.monotonic() - gen_started:.1f}{self._request_log_suffix()}",
+                        flush=True,
                     )
                 else:
                     text = backend.generate_text(messages, max_tokens, temperature, top_p)
+                    completion_tokens = _backend_token_count(
+                        backend, "count_text_tokens", text, fallback=len(text.split()) or 1,
+                    )
+                    print(
+                        f"  [node] chat complete tokens={completion_tokens} "
+                        f"elapsed_s={time.monotonic() - gen_started:.1f}{self._request_log_suffix()}",
+                        flush=True,
+                    )
                     self._send_openai_response(text, model_name, False, messages, backend=backend)
             except Exception as exc:
                 self._record_failed_request()
+                print(
+                    f"  [node] chat failed after {time.monotonic() - gen_started:.1f}s: {exc}"
+                    f"{self._request_log_suffix()}",
+                    flush=True,
+                )
                 self._send_json(500, {"error": f"generation failed: {exc}"})
             return