Move sampler into CUDA graph (#1201)

Co-authored-by: Yineng Zhang <me@zhyncs.com>

python/sglang/srt/models/minicpm.py

@@ -39,6 +39,7 @@ from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.sampler import Sampler
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
 
 
@@ -297,6 +298,7 @@ class MiniCPMForCausalLM(nn.Module):
         self.scale_width = self.config.hidden_size / self.config.dim_model_base
 
         self.logits_processor = LogitsProcessor(config)
+        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -314,9 +316,11 @@ class MiniCPMForCausalLM(nn.Module):
             lm_head_weight = self.model.embed_tokens.weight
         else:
             lm_head_weight = self.lm_head.weight
-        return self.logits_processor(
+        logits_output = self.logits_processor(
             input_ids, hidden_states, lm_head_weight, input_metadata
         )
+        sample_output = self.sampler(logits_output, input_metadata.sampling_info)
+        return sample_output, logits_output
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [