[Model] Support IQuest-Coder-40B-Loop

python/sglang/srt/configs/model_config.py

@@ -463,6 +463,9 @@ def _derive_model_shapes(self):
         self.num_attention_layers = self.num_hidden_layers
         if "LongcatFlashForCausalLM" in self.hf_config.architectures:
             self.num_attention_layers = self.num_hidden_layers * 2
+        if "IQuestLoopCoderForCausalLM" in self.hf_config.architectures:
+            loop_num = getattr(self.hf_text_config, "loop_num", 1)
+            self.num_attention_layers = int(self.num_hidden_layers * int(loop_num))
         self.num_nextn_predict_layers = getattr(
             self.hf_text_config, "num_nextn_predict_layers", None
         )

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -793,6 +793,13 @@ def forward_extend(
                 v_scale=layer.v_scale_float,
             )
         else:
+            # If `k`/`v` are not explicitly provided, fall back to the KV cache stored in
+            # `forward_batch.token_to_kv_pool` for this layer. This enables attention over
+            # previously cached context without re-materializing KV tensors (e.g., the
+            # IQuestLoopCoder path uses token_to_kv_pool as the KV source).
+            if k is None and v is None:
+                k = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)[0]
+                v = forward_batch.token_to_kv_pool.get_kv_buffer(layer.layer_id)[1]
             causal = True
             if (
                 layer.is_cross_attention

python/sglang/srt/model_executor/model_runner.py

@@ -485,6 +485,12 @@ def initialize(self, min_per_gpu_memory: float):
         self.start_layer = getattr(self.model, "start_layer", 0)
         self.end_layer = getattr(self.model, "end_layer", model_num_layers)
         self.num_effective_layers = self.end_layer - self.start_layer
+
+        # For LoopCoder models, each loop has its own layer_id, so we need to multiply by loop_num
+        loop_num = getattr(self.model_config.hf_config, "loop_num", 1)
+        if loop_num > 1:
+            self.num_effective_layers = self.num_effective_layers * loop_num
+
         assert (
             (not model_has_mtp_layers)
             or (self.spec_algorithm.is_none())