Sampler cudagraph (#1253)

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 = [

python/sglang/srt/models/mixtral.py

@@ -41,6 +41,7 @@ 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
 
 
@@ -299,6 +300,7 @@ 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,
@@ -308,9 +310,11 @@ class MixtralForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        return self.logits_processor(
+        logits_output = 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

@@ -45,6 +45,7 @@ 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
 
 
@@ -333,6 +334,7 @@ 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(
@@ -343,9 +345,11 @@ class QuantMixtralForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        return self.logits_processor(
+        logits_output = 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

@@ -39,6 +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
 
 
@@ -251,6 +252,7 @@ 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(
@@ -260,10 +262,11 @@ class QWenLMHeadModel(nn.Module):
         input_metadata: InputMetadata,
     ):
         hidden_states = self.transformer(input_ids, positions, input_metadata)
-        next_tokens = self.logits_processor(
+        logits_output = self.logits_processor(
             input_ids, hidden_states, self.lm_head.weight, input_metadata
         )
-        return next_tokens
+        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

@@ -38,8 +38,9 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 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.pooler import EmbeddingPoolerOutput, Pooler, PoolingType
+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
@@ -276,6 +277,7 @@ 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()
@@ -289,9 +291,11 @@ class Qwen2ForCausalLM(nn.Module):
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
         if not get_embedding:
-            return self.logits_processor(
+            logits_output = 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

@@ -35,10 +35,8 @@ from vllm.model_executor.layers.linear import (
     ReplicatedLinear,
     RowParallelLinear,
 )
-from vllm.model_executor.layers.logits_processor import LogitsProcessor
 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,
@@ -49,6 +47,7 @@ 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
 
 
@@ -366,6 +365,7 @@ 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(
@@ -376,20 +376,11 @@ class Qwen2MoeForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        return self.logits_processor(
+        logits_output = self.logits_processor(
             input_ids, hidden_states, self.lm_head.weight, input_metadata
         )
-
-    def compute_logits(
-        self,
-        input_ids: torch.Tensor,
-        hidden_states: torch.Tensor,
-        input_metadata: InputMetadata,
-    ) -> torch.Tensor:
-        logits = self.logits_processor(
-            input_ids, hidden_states, self.lm_head.weight, input_metadata
-        )
-        return logits
+        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

@@ -40,6 +40,7 @@ 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
 
 
@@ -249,6 +250,7 @@ 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(
@@ -259,9 +261,11 @@ class StableLmForCausalLM(nn.Module):
         input_embeds: torch.Tensor = None,
     ) -> torch.Tensor:
         hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
-        return self.logits_processor(
+        logits_output = 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

@@ -21,10 +21,63 @@ class SamplingBatchInfo:
     top_ps: torch.Tensor = None
     top_ks: torch.Tensor = None
     min_ps: torch.Tensor = None
-    penalizer_orchestrator: penaltylib.BatchedPenalizerOrchestrator = None
+
+    # Dispatch in CUDA graph
+    need_min_p_sampling: bool = False
+
+    # Bias Tensors
     logit_bias: torch.Tensor = None
     vocab_mask: torch.Tensor = None
 
+    # Penalizer
+    penalizer_orchestrator: penaltylib.BatchedPenalizerOrchestrator = None
+    linear_penalties: torch.Tensor = None
+    scaling_penalties: torch.Tensor = None
+
+    def has_bias(self):
+        return (
+            self.logit_bias is not None
+            or self.vocab_mask is not None
+            or self.linear_penalties is not None
+            or self.scaling_penalties is not None
+        )
+
+    @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")
+        ret.min_ps = torch.zeros((max_bs,), dtype=torch.float, device="cuda")
+        return ret
+
+    def __getitem__(self, key):
+        if isinstance(key, slice):
+            # NOTE: We do not use cuda graph when there is bias tensors
+            assert not self.has_bias()
+            return SamplingBatchInfo(
+                vocab_size=self.vocab_size,
+                temperatures=self.temperatures[key],
+                top_ps=self.top_ps[key],
+                top_ks=self.top_ks[key],
+                min_ps=self.min_ps[key],
+                need_min_p_sampling=self.need_min_p_sampling,
+            )
+        else:
+            raise NotImplementedError
+
+    def inplace_assign(self, bs: int, other: SamplingBatchInfo):
+        # NOTE: We do not use cuda graph when there is bias tensors
+        assert not self.has_bias()
+
+        self.vocab_size = other.vocab_size
+        self.need_min_p_sampling = other.need_min_p_sampling
+
+        self.temperatures[:bs] = other.temperatures
+        self.top_ps[:bs] = other.top_ps
+        self.top_ks[:bs] = other.top_ks
+        self.min_ps[:bs] = other.min_ps
+
     @classmethod
     def from_schedule_batch(cls, batch: ScheduleBatch, vocab_size: int):
         device = "cuda"
@@ -45,6 +98,7 @@ class SamplingBatchInfo:
         ret.min_ps = torch.tensor(
             [r.sampling_params.min_p for r in reqs], dtype=torch.float, device=device
         )
+        ret.need_min_p_sampling = any(r.sampling_params.min_p > 0 for r in reqs)
 
         # Each penalizers will do nothing if they evaluate themselves as not required by looking at
         # the sampling_params of the requests (See {_is_required()} of each penalizers). So this
@@ -72,6 +126,25 @@ class SamplingBatchInfo:
 
         return ret
 
+    def prepare_penalties(self):
+        self.scaling_penalties = None
+        self.linear_penalties = None
+
+        for penalizer in self.penalizer_orchestrator.penalizers.values():
+            if isinstance(penalizer, penaltylib.BatchedRepetitionPenalizer):
+                if penalizer.is_prepared():
+                    self.scaling_penalties = penalizer.cumulated_repetition_penalties
+            else:
+                if penalizer.is_prepared():
+                    if self.linear_penalties is None:
+                        bs = self.penalizer_orchestrator.batch.batch_size()
+                        self.linear_penalties = torch.zeros(
+                            (bs, self.vocab_size),
+                            dtype=torch.float32,
+                            device="cuda",
+                        )
+                    self.linear_penalties = penalizer.apply(self.linear_penalties)
+
     def update_regex_vocab_mask(self, batch: ScheduleBatch):
         bs, reqs = batch.batch_size(), batch.reqs
         device = "cuda"

python/sglang/test/runners.py

@@ -180,7 +180,7 @@ class SRTRunner:
             tp_size=tp_size,
             dtype=get_dtype_str(torch_dtype),
             port=port,
-            mem_fraction_static=0.7,
+            mem_fraction_static=0.69,
             trust_remote_code=False,
             is_embedding=not self.is_generation,
         )