hotfix: revert sampler CUDA Graph (#1242)

python/sglang/srt/models/chatglm.py

@@ -31,18 +31,20 @@ from vllm.model_executor.layers.linear import (
 )
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs import ChatGLMConfig
 
 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
 
 LoraConfig = None
@@ -381,11 +383,17 @@ class ChatGLMForCausalLM(nn.Module):
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, input_metadata)
-        logits_output = self.logits_processor(
+        return self.logits_processor(
             input_ids, hidden_states, self.lm_head.weight, input_metadata
         )
-        sample_output = self.sampler(logits_output, input_metadata.sampling_info)
-        return sample_output, logits_output
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[SamplerOutput]:
+        next_tokens = self.sampler(logits, sampling_metadata)
+        return next_tokens
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         params_dict = dict(self.named_parameters(remove_duplicate=False))