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

python/sglang/bench_latency.py

@@ -207,15 +207,15 @@ def extend(reqs, model_runner):
         tree_cache=None,
     )
     batch.prepare_for_extend(model_runner.model_config.vocab_size)
-    sample_output, logits_output = model_runner.forward(batch)
-    next_token_ids = sample_output.batch_next_token_ids.tolist()
+    logits_output = model_runner.forward(batch)
+    next_token_ids = model_runner.sample(logits_output, batch).tolist()
     return next_token_ids, logits_output.next_token_logits, batch
 
 
 def decode(input_token_ids, batch, model_runner):
     batch.prepare_for_decode(input_token_ids)
-    sample_output, logits_output = model_runner.forward(batch)
-    next_token_ids = sample_output.batch_next_token_ids.tolist()
+    logits_output = model_runner.forward(batch)
+    next_token_ids = model_runner.sample(logits_output, batch).tolist()
     return next_token_ids, logits_output.next_token_logits
 
 

python/sglang/srt/layers/sampler.py

@@ -35,21 +35,6 @@ class Sampler(CustomOp):
         self.forward_native = self.forward_cuda
         self.is_torch_compile = False
 
-    def _apply_penalties(self, logits: torch.Tensor, sampling_info: SamplingBatchInfo):
-        # min-token, presence, frequency
-        if sampling_info.linear_penalties is not None:
-            logits += sampling_info.linear_penalties
-
-        # repetition
-        if sampling_info.scaling_penalties is not None:
-            logits = torch.where(
-                logits > 0,
-                logits / sampling_info.scaling_penalties,
-                logits * sampling_info.scaling_penalties,
-            )
-
-        return logits
-
     def _get_probs(self, logits: torch.Tensor, sampling_info: SamplingBatchInfo):
         # Post process logits
         logits = logits.contiguous()
@@ -58,14 +43,6 @@ class Sampler(CustomOp):
             # FIXME: Temporary workaround for unknown bugs in torch.compile
             logits.add_(0)
 
-        if sampling_info.logit_bias is not None:
-            logits.add_(sampling_info.logit_bias)
-
-        if sampling_info.vocab_mask is not None:
-            logits = logits.masked_fill(sampling_info.vocab_mask, float("-inf"))
-
-        logits = self._apply_penalties(logits, sampling_info)
-
         return torch.softmax(logits, dim=-1)
 
     def forward_cuda(

python/sglang/srt/managers/schedule_batch.py

@@ -33,10 +33,6 @@ from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.server_args import ServerArgs
 
-if TYPE_CHECKING:
-    from sglang.srt.layers.sampler import SampleOutput
-
-
 INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
 
 # Put some global args for easy access
@@ -710,18 +706,3 @@ class ScheduleBatch:
         self.out_cache_loc = None
         self.top_logprobs_nums.extend(other.top_logprobs_nums)
         self.return_logprob = any(req.return_logprob for req in self.reqs)
-
-    def check_sample_results(self, sample_output: SampleOutput):
-        if not torch.all(sample_output.success):
-            probs = sample_output.probs
-            batch_next_token_ids = sample_output.batch_next_token_ids
-            logging.warning("Sampling failed, fallback to top_k=1 strategy")
-            probs = probs.masked_fill(torch.isnan(probs), 0.0)
-            argmax_ids = torch.argmax(probs, dim=-1)
-            batch_next_token_ids = torch.where(
-                sample_output.success, batch_next_token_ids, argmax_ids
-            )
-            sample_output.probs = probs
-            sample_output.batch_next_token_ids = batch_next_token_ids
-
-        return sample_output.batch_next_token_ids

python/sglang/srt/managers/tp_worker.py

@@ -547,8 +547,9 @@ class ModelTpServer:
         if self.model_runner.is_generation:
             # Forward and sample the next tokens
             if batch.extend_num_tokens != 0:
-                sample_output, logits_output = self.model_runner.forward(batch)
-                next_token_ids = batch.check_sample_results(sample_output)
+                logits_output = self.model_runner.forward(batch)
+                next_token_ids = self.model_runner.sample(logits_output, batch)
+
                 batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
                     next_token_ids
                 )
@@ -723,8 +724,8 @@ class ModelTpServer:
         batch.prepare_for_decode()
 
         # Forward and sample the next tokens
-        sample_output, logits_output = self.model_runner.forward(batch)
-        next_token_ids = batch.check_sample_results(sample_output)
+        logits_output = self.model_runner.forward(batch)
+        next_token_ids = self.model_runner.sample(logits_output, batch)
         batch.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
             next_token_ids
         )

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -30,10 +30,8 @@ from sglang.srt.layers.logits_processor import (
     LogitsProcessor,
     LogitsProcessorOutput,
 )
-from sglang.srt.layers.sampler import SampleOutput
 from sglang.srt.managers.schedule_batch import ScheduleBatch
 from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
-from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.utils import monkey_patch_vllm_all_gather
 
 if TYPE_CHECKING:
@@ -129,10 +127,6 @@ class CudaGraphRunner:
             self.model_runner.attn_backend.get_cuda_graph_seq_len_fill_value()
         )
 
-        # Sampling info
-        vocab_size = model_runner.model_config.vocab_size
-        self.sampling_info = SamplingBatchInfo.dummy_one(self.max_bs, vocab_size)
-
         if self.use_torch_compile:
             set_torch_compile_config()
 
@@ -191,7 +185,6 @@ class CudaGraphRunner:
         def run_once():
             input_metadata = InputMetadata(
                 forward_mode=ForwardMode.DECODE,
-                sampling_info=self.sampling_info[:bs],
                 batch_size=bs,
                 req_pool_indices=req_pool_indices,
                 seq_lens=seq_lens,
@@ -250,14 +243,9 @@ class CudaGraphRunner:
             bs, self.req_pool_indices, self.seq_lens
         )
 
-        # Sampling inputs
-        self.sampling_info.inplace_assign(raw_bs, batch.sampling_info)
-
         # Replay
-        torch.cuda.synchronize()
         self.graphs[bs].replay()
-        torch.cuda.synchronize()
-        sample_output, logits_output = self.output_buffers[bs]
+        logits_output = self.output_buffers[bs]
 
         # Unpad
         if bs != raw_bs:
@@ -269,11 +257,6 @@ class CudaGraphRunner:
                 input_top_logprobs=None,
                 output_top_logprobs=None,
             )
-            sample_output = SampleOutput(
-                sample_output.success[:raw_bs],
-                sample_output.probs[:raw_bs],
-                sample_output.batch_next_token_ids[:raw_bs],
-            )
 
         # Extract logprobs
         if batch.return_logprob:
@@ -290,4 +273,4 @@ class CudaGraphRunner:
                     logits_output.next_token_logprobs, logits_metadata
                 )[1]
 
-        return sample_output, logits_output
+        return logits_output

python/sglang/srt/model_executor/forward_batch_info.py

@@ -28,7 +28,6 @@ if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import ScheduleBatch
     from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool
     from sglang.srt.model_executor.model_runner import ModelRunner
-    from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 
 
 class ForwardMode(IntEnum):
@@ -59,7 +58,6 @@ class InputMetadata:
     """Store all inforamtion of a forward pass."""
 
     forward_mode: ForwardMode
-    sampling_info: SamplingBatchInfo
     batch_size: int
     req_pool_indices: torch.Tensor
     seq_lens: torch.Tensor
@@ -170,7 +168,6 @@ class InputMetadata:
     ):
         ret = cls(
             forward_mode=batch.forward_mode,
-            sampling_info=batch.sampling_info,
             batch_size=batch.batch_size(),
             req_pool_indices=batch.req_pool_indices,
             seq_lens=batch.seq_lens,
@@ -182,8 +179,6 @@ class InputMetadata:
             top_logprobs_nums=batch.top_logprobs_nums,
         )
 
-        ret.sampling_info.update_penalties()
-        ret.sampling_info.update_regex_vocab_mask(batch)
         ret.compute_positions(batch)
 
         if not batch.forward_mode.is_decode():

python/sglang/srt/model_executor/model_runner.py

@@ -40,7 +40,7 @@ from vllm.model_executor.models import ModelRegistry
 from sglang.srt.configs.model_config import AttentionArch, ModelConfig
 from sglang.srt.layers.attention_backend import FlashInferAttnBackend, TritonAttnBackend
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.layers.sampler import SampleOutput
+from sglang.srt.layers.sampler import SampleOutput, Sampler
 from sglang.srt.lora.lora_manager import LoRAManager
 from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
 from sglang.srt.mem_cache.memory_pool import (
@@ -49,6 +49,7 @@ from sglang.srt.mem_cache.memory_pool import (
     ReqToTokenPool,
 )
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
+from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_available_gpu_memory,
@@ -107,6 +108,7 @@ class ModelRunner:
 
         # Init componnets
         min_per_gpu_memory = self.init_torch_distributed()
+        self.sampler = Sampler()
         self.load_model()
         if server_args.lora_paths is not None:
             self.init_lora_manager()
@@ -466,11 +468,8 @@ class ModelRunner:
     def forward_decode(self, batch: ScheduleBatch):
         if self.server_args.lora_paths is not None:
             self.lora_manager.prepare_lora_batch(batch)
-        if (
-            self.cuda_graph_runner
-            and self.cuda_graph_runner.can_run(len(batch.reqs))
-            and batch.sampling_info.can_run_in_cuda_graph()
-        ):
+
+        if self.cuda_graph_runner and self.cuda_graph_runner.can_run(len(batch.reqs)):
             return self.cuda_graph_runner.replay(batch)
 
         input_metadata = InputMetadata.from_schedule_batch(self, batch)
@@ -510,9 +509,7 @@ class ModelRunner:
             input_metadata.image_offsets,
         )
 
-    def forward(
-        self, batch: ScheduleBatch
-    ) -> Tuple[SampleOutput, LogitsProcessorOutput]:
+    def forward(self, batch: ScheduleBatch) -> Tuple[LogitsProcessorOutput]:
         assert batch.forward_mode is not None
 
         if self.is_multimodal_model and batch.forward_mode.is_extend():
@@ -524,6 +521,57 @@ class ModelRunner:
         else:
             raise ValueError(f"Invaid forward mode: {batch.forward_mode}")
 
+    def _check_sample_results(self, sample_output: SampleOutput):
+        if not torch.all(sample_output.success):
+            probs = sample_output.probs
+            batch_next_token_ids = sample_output.batch_next_token_ids
+            logging.warning("Sampling failed, fallback to top_k=1 strategy")
+            probs = probs.masked_fill(torch.isnan(probs), 0.0)
+            argmax_ids = torch.argmax(probs, dim=-1)
+            batch_next_token_ids = torch.where(
+                sample_output.success, batch_next_token_ids, argmax_ids
+            )
+            sample_output.probs = probs
+            sample_output.batch_next_token_ids = batch_next_token_ids
+
+        return sample_output.batch_next_token_ids
+
+    def _apply_logits_bias(
+        self, logits: torch.Tensor, sampling_info: SamplingBatchInfo
+    ):
+        # Apply logit_bias
+        if sampling_info.logit_bias is not None:
+            logits.add_(sampling_info.logit_bias)
+
+        # min-token, presence, frequency
+        if sampling_info.linear_penalties is not None:
+            logits += sampling_info.linear_penalties
+
+        # repetition
+        if sampling_info.scaling_penalties is not None:
+            logits = torch.where(
+                logits > 0,
+                logits / sampling_info.scaling_penalties,
+                logits * sampling_info.scaling_penalties,
+            )
+
+        # Apply regex vocab_mask
+        if sampling_info.vocab_mask is not None:
+            logits = logits.masked_fill(sampling_info.vocab_mask, float("-inf"))
+
+        return logits
+
+    def sample(
+        self, logits_output: LogitsProcessorOutput, batch: ScheduleBatch
+    ) -> torch.Tensor:
+        batch.sampling_info.update_regex_vocab_mask(batch)
+        batch.sampling_info.update_penalties()
+        logits = self._apply_logits_bias(
+            logits_output.next_token_logits, batch.sampling_info
+        )
+        sample_output = self.sampler(logits, batch.sampling_info)
+        return self._check_sample_results(sample_output)
+
 
 @lru_cache()
 def import_model_classes():

python/sglang/srt/models/baichuan.py

@@ -46,7 +46,6 @@ 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
 
 
@@ -346,7 +345,6 @@ class BaiChuanBaseForCausalLM(nn.Module):
         if self.config.tie_word_embeddings:
             self.lm_head.weight = self.model.embed_tokens.weight
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     def forward(
         self,
@@ -355,12 +353,9 @@ class BaiChuanBaseForCausalLM(nn.Module):
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
         hidden_states = self.model(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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/chatglm.py

@@ -42,7 +42,6 @@ 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
 
 LoraConfig = None
@@ -371,7 +370,6 @@ class ChatGLMForCausalLM(nn.Module):
         self.transformer = ChatGLMModel(config, cache_config, quant_config)
         self.lm_head = self.transformer.output_layer
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -381,11 +379,9 @@ 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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         params_dict = dict(self.named_parameters(remove_duplicate=False))

python/sglang/srt/models/commandr.py

@@ -64,7 +64,6 @@ from vllm.model_executor.utils import set_weight_attrs
 from sglang.srt.layers.activation import SiluAndMul
 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
 
 
@@ -327,7 +326,6 @@ class CohereForCausalLM(nn.Module):
         self.config = config
         self.quant_config = quant_config
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
         self.model = CohereModel(config, quant_config)
 
     @torch.no_grad()
@@ -342,11 +340,9 @@ class CohereForCausalLM(nn.Module):
             positions,
             input_metadata,
         )
-        logits_output = self.logits_processor(
+        return self.logits_processor(
             input_ids, hidden_states, self.model.embed_tokens.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 = [

python/sglang/srt/models/dbrx.py

@@ -45,7 +45,6 @@ from vllm.transformers_utils.configs.dbrx import DbrxConfig
 
 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
 
 
@@ -383,7 +382,6 @@ class DbrxForCausalLM(nn.Module):
             padding_size=DEFAULT_VOCAB_PADDING_SIZE,
         )
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -393,11 +391,9 @@ class DbrxForCausalLM(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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         expert_params_mapping = [

python/sglang/srt/models/deepseek.py

@@ -46,7 +46,6 @@ 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
 
 
@@ -386,7 +385,6 @@ class DeepseekForCausalLM(nn.Module):
             config.vocab_size, config.hidden_size, quant_config=quant_config
         )
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -396,11 +394,9 @@ class DeepseekForCausalLM(nn.Module):
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
         hidden_states = self.model(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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/deepseek_v2.py

@@ -46,7 +46,6 @@ 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.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
 
@@ -649,7 +648,6 @@ class DeepseekV2ForCausalLM(nn.Module):
             config.vocab_size, config.hidden_size, quant_config=quant_config
         )
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     def forward(
         self,
@@ -658,11 +656,9 @@ class DeepseekV2ForCausalLM(nn.Module):
         input_metadata: InputMetadata,
     ) -> torch.Tensor:
         hidden_states = self.model(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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/exaone.py

@@ -40,7 +40,6 @@ from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
 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
 
 
@@ -304,7 +303,6 @@ class ExaoneForCausalLM(nn.Module):
         self.transformer = ExaoneModel(config, quant_config=quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -317,11 +315,9 @@ class ExaoneForCausalLM(nn.Module):
         hidden_states = self.transformer(
             input_ids, positions, input_metadata, input_embeds
         )
-        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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/gemma.py

@@ -37,7 +37,6 @@ from sglang.srt.layers.activation import GeluAndMul
 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
 
 
@@ -288,7 +287,6 @@ class GemmaForCausalLM(nn.Module):
         self.quant_config = quant_config
         self.model = GemmaModel(config, quant_config=quant_config)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -299,11 +297,9 @@ class GemmaForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        logits_output = self.logits_processor(
+        return self.logits_processor(
             input_ids, hidden_states, self.model.embed_tokens.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 = [

python/sglang/srt/models/gemma2.py

@@ -37,7 +37,6 @@ from sglang.srt.layers.activation import GeluAndMul
 from sglang.srt.layers.layernorm import GemmaRMSNorm
 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
 
 
@@ -347,7 +346,6 @@ class Gemma2ForCausalLM(nn.Module):
         self.quant_config = quant_config
         self.model = Gemma2Model(config, cache_config, quant_config)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -358,11 +356,9 @@ class Gemma2ForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        logits_output = self.logits_processor(
+        return self.logits_processor(
             input_ids, hidden_states, self.model.embed_tokens.weight, input_metadata
         )
-        sample_output = self.sampler(logits_output, input_metadata.sampling_info)
-        return sample_output, logits_output
 
     def get_attention_sliding_window_size(self):
         return get_attention_sliding_window_size(self.config)

python/sglang/srt/models/gpt_bigcode.py

@@ -35,7 +35,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from sglang.srt.layers.activation import get_act_fn
 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
 
 
@@ -262,7 +261,6 @@ class GPTBigCodeForCausalLM(nn.Module):
         if lora_config:
             self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -272,11 +270,9 @@ class GPTBigCodeForCausalLM(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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         params_dict = dict(self.named_parameters(remove_duplicate=False))

python/sglang/srt/models/grok.py

@@ -46,7 +46,6 @@ from sglang.srt.layers.fused_moe import FusedMoE
 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 Grok1ForCausalLM(nn.Module):
         self.model = Grok1Model(config, quant_config=quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
         # Monkey patch _prepare_weights to load pre-sharded weights
         setattr(DefaultModelLoader, "_prepare_weights", _prepare_presharded_weights)
@@ -315,11 +313,9 @@ class Grok1ForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        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 load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         stacked_params_mapping = [

python/sglang/srt/models/internlm2.py

@@ -40,7 +40,6 @@ 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
 
 
@@ -263,7 +262,6 @@ class InternLM2ForCausalLM(nn.Module):
         self.model = InternLM2Model(config, quant_config)
         self.output = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -274,11 +272,9 @@ class InternLM2ForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        logits_output = self.logits_processor(
+        return self.logits_processor(
             input_ids, hidden_states, self.output.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 = [

python/sglang/srt/models/llama.py

@@ -41,7 +41,6 @@ from sglang.srt.layers.activation import SiluAndMul
 from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
 from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.layers.sampler import Sampler
 from sglang.srt.layers.torchao_utils import torchao_quantize_param_data
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
@@ -305,7 +304,6 @@ class LlamaForCausalLM(nn.Module):
         self.model = LlamaModel(config, quant_config=quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
         self.param_dict = dict(self.named_parameters())
 
@@ -318,11 +316,9 @@ class LlamaForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> LogitsProcessorOutput:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        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 get_hidden_dim(self, module_name):
         if module_name in ["q_proj", "o_proj", "qkv_proj"]: