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

python/sglang/srt/models/llama_classification.py

@@ -23,7 +23,6 @@ from vllm.model_executor.layers.quantization.base_config import QuantizationConf
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
-from sglang.srt.layers.sampler import SampleOutput
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
 from sglang.srt.models.llama import LlamaForCausalLM, LlamaModel
 
@@ -75,25 +74,7 @@ class LlamaForClassification(nn.Module):
             output_top_logprobs=None,
         )
 
-        # A dummy to make this work
-        sample_output = SampleOutput(
-            success=torch.full(
-                size=(scores.shape[0],),
-                fill_value=True,
-                dtype=torch.bool,
-            ),
-            probs=torch.full(
-                size=(scores.shape[0], 1),
-                fill_value=1.0,
-                dtype=torch.float16,
-            ),
-            batch_next_token_ids=torch.full(
-                size=(scores.shape[0],),
-                fill_value=0,
-                dtype=torch.long,
-            ),
-        )
-        return sample_output, logits_output
+        return logits_output
 
     def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
         params_dict = self.param_dict

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 = [

python/sglang/srt/models/minicpm3.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.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import InputMetadata
 
@@ -572,7 +571,6 @@ class MiniCPM3ForCausalLM(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(
@@ -590,11 +588,9 @@ class MiniCPM3ForCausalLM(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 = [

python/sglang/srt/models/mixtral.py

@@ -41,7 +41,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 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
 
 
@@ -300,7 +299,6 @@ class MixtralForCausalLM(nn.Module):
         self.model = MixtralModel(config, quant_config=quant_config, prefix="model")
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     def forward(
         self,
@@ -310,11 +308,9 @@ class MixtralForCausalLM(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/mixtral_quant.py

@@ -45,7 +45,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 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
 
 
@@ -334,7 +333,6 @@ class QuantMixtralForCausalLM(nn.Module):
         self.model = MixtralModel(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(
@@ -345,11 +343,9 @@ class QuantMixtralForCausalLM(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/qwen.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
 
 
@@ -252,7 +251,6 @@ class QWenLMHeadModel(nn.Module):
         vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.lm_head = ParallelLMHead(vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
     @torch.no_grad()
     def forward(
@@ -262,11 +260,9 @@ class QWenLMHeadModel(nn.Module):
         input_metadata: InputMetadata,
     ):
         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]]):
         stacked_params_mapping = [

python/sglang/srt/models/qwen2.py

@@ -40,7 +40,6 @@ from sglang.srt.layers.layernorm import RMSNorm
 from sglang.srt.layers.logits_processor import LogitsProcessor
 from sglang.srt.layers.pooler import Pooler, PoolingType
 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
 
 Qwen2Config = None
@@ -277,7 +276,6 @@ class Qwen2ForCausalLM(nn.Module):
         self.model = Qwen2Model(config, quant_config=quant_config)
         self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
         self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
 
     @torch.no_grad()
@@ -291,11 +289,9 @@ class Qwen2ForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
         if not get_embedding:
-            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
         else:
             return self.pooler(hidden_states, input_metadata)
 

python/sglang/srt/models/qwen2_moe.py

@@ -47,7 +47,6 @@ 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
 
 
@@ -365,7 +364,6 @@ class Qwen2MoeForCausalLM(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(
@@ -376,11 +374,9 @@ class Qwen2MoeForCausalLM(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/stablelm.py

@@ -40,7 +40,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 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
 
 
@@ -250,7 +249,6 @@ class StableLmForCausalLM(nn.Module):
         self.model = StableLMEpochModel(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(
@@ -261,11 +259,9 @@ class StableLmForCausalLM(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/xverse.py

@@ -41,7 +41,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 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.model_runner import InputMetadata
 
 
@@ -307,7 +306,6 @@ class XverseForCausalLM(nn.Module):
         self.model = XverseModel(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())
 
@@ -320,12 +318,9 @@ class XverseForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        # print(f"{hidden_states=}")
-        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]], name=None, loaded_weight=None

python/sglang/srt/models/xverse_moe.py

@@ -44,7 +44,6 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 
 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 XverseMoeForCausalLM(nn.Module):
             config.vocab_size, config.hidden_size, quant_config=quant_config
         )
         self.logits_processor = LogitsProcessor(config)
-        self.sampler = Sampler()
 
         self.param_dict = dict(self.named_parameters())
 
@@ -395,11 +393,9 @@ class XverseMoeForCausalLM(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/sampling/sampling_batch_info.py

@@ -41,7 +41,6 @@ class SamplingBatchInfo:
         # Vocab bias and min_ps are not supported in CUDA graph
         return (
             self.logit_bias is None
-            and self.vocab_mask is None
             and self.linear_penalties is None
             and self.scaling_penalties is None
             and not self.need_min_p_sampling
@@ -50,9 +49,11 @@ class SamplingBatchInfo:
     @classmethod
     def dummy_one(cls, max_bs: int, vocab_size: int):
         ret = cls(vocab_size=vocab_size)
-        ret.temperatures = torch.ones((max_bs, 1), dtype=torch.float, device="cuda")
-        ret.top_ps = torch.ones((max_bs,), dtype=torch.float, device="cuda")
-        ret.top_ks = torch.ones((max_bs,), dtype=torch.int, device="cuda")
+        with torch.device("cuda"):
+            ret.temperatures = torch.ones((max_bs, 1), dtype=torch.float)
+            ret.top_ps = torch.ones((max_bs,), dtype=torch.float)
+            ret.top_ks = torch.ones((max_bs,), dtype=torch.int)
+            ret.vocab_mask = torch.zeros((max_bs, vocab_size), dtype=torch.bool)
         return ret
 
     def __getitem__(self, key):
@@ -64,6 +65,7 @@ class SamplingBatchInfo:
                 temperatures=self.temperatures[key],
                 top_ps=self.top_ps[key],
                 top_ks=self.top_ks[key],
+                vocab_mask=self.vocab_mask[key],
             )
         else:
             raise NotImplementedError
@@ -77,6 +79,11 @@ class SamplingBatchInfo:
         self.top_ps[:bs] = other.top_ps
         self.top_ks[:bs] = other.top_ks
 
+        if other.vocab_mask is None:
+            self.vocab_mask[:bs].fill_(False)
+        else:
+            self.vocab_mask[:bs] = other.vocab_mask
+
     @classmethod
     def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
         device = "cuda"