Optimize conflicts between CUDA graph and vocab mask tensors (#1392)

python/sglang/srt/models/minicpm.py

@@ -39,7 +39,6 @@ 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
 
 
@@ -298,7 +297,6 @@ 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(
@@ -316,11 +314,9 @@ class MiniCPMForCausalLM(nn.Module):
             lm_head_weight = self.model.embed_tokens.weight
         else:
             lm_head_weight = self.lm_head.weight
-        logits_output = self.logits_processor(
+        return 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 = [