Eagle speculative decoding part 3: small modifications to the general scheduler (#2709)

Co-authored-by: kavioyu <kavioyu@tencent.com>

benchmark/deepseek_v3/README.md

@@ -61,10 +61,10 @@ For example, there are two H20 nodes, each with 8 GPUs. The first node's IP is `
 
 ```bash
 # node 1
-python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
+python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --dist-init-addr 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
 
 # node 2
-python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
+python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --dist-init-addr 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
 ```
 
 If you have two H100 nodes, the usage is similar to the aforementioned H20.

python/sglang/bench_one_batch.py

@@ -63,6 +63,7 @@ from sglang.srt.model_executor.model_runner import ModelRunner
 from sglang.srt.sampling.sampling_params import SamplingParams
 from sglang.srt.server import _set_envs_and_config
 from sglang.srt.server_args import PortArgs, ServerArgs
+from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import configure_logger, kill_process_tree, suppress_other_loggers
 
 
@@ -214,6 +215,7 @@ def extend(reqs, model_runner):
         tree_cache=None,
         model_config=model_runner.model_config,
         enable_overlap=False,
+        spec_algorithm=SpeculativeAlgorithm.NONE,
     )
     batch.prepare_for_extend()
     model_worker_batch = batch.get_model_worker_batch()

python/sglang/srt/layers/attention/__init__.py

@@ -26,7 +26,7 @@ class AttentionBackend(ABC):
     def init_forward_metadata_capture_cuda_graph(
         self,
         bs: int,
-        num_token: int,
+        num_tokens: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         encoder_lens: Optional[torch.Tensor],

python/sglang/srt/layers/attention/flashinfer_backend.py

@@ -227,7 +227,7 @@ class FlashInferAttnBackend(AttentionBackend):
     def init_forward_metadata_capture_cuda_graph(
         self,
         bs: int,
-        num_token: int,
+        num_tokens: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         encoder_lens: Optional[torch.Tensor],
@@ -243,9 +243,11 @@ class FlashInferAttnBackend(AttentionBackend):
                         "NHD",
                         use_cuda_graph=True,
                         use_tensor_cores=self.decode_use_tensor_cores,
-                        paged_kv_indptr_buffer=self.kv_indptr[i][: num_token + 1],
+                        paged_kv_indptr_buffer=self.kv_indptr[i][: num_tokens + 1],
                         paged_kv_indices_buffer=self.cuda_graph_kv_indices[i],
-                        paged_kv_last_page_len_buffer=self.kv_last_page_len[:num_token],
+                        paged_kv_last_page_len_buffer=self.kv_last_page_len[
+                            :num_tokens
+                        ],
                     )
                 )
             seq_lens_sum = seq_lens.sum().item()

python/sglang/srt/layers/attention/triton_backend.py

@@ -81,7 +81,7 @@ class TritonAttnBackend(AttentionBackend):
     def init_forward_metadata_capture_cuda_graph(
         self,
         bs: int,
-        num_token: int,
+        num_tokens: int,
         req_pool_indices: torch.Tensor,
         seq_lens: torch.Tensor,
         encoder_lens: Optional[torch.Tensor],

python/sglang/srt/managers/schedule_batch.py

@@ -575,8 +575,8 @@ class ScheduleBatch:
     device: str = "cuda"
 
     # Speculative decoding
+    spec_algorithm: SpeculativeAlgorithm = None
     spec_info: Optional[SpecInfo] = None
-    spec_algorithm: Optional[SpeculativeAlgorithm] = None
 
     @classmethod
     def init_new(
@@ -587,7 +587,7 @@ class ScheduleBatch:
         tree_cache: BasePrefixCache,
         model_config: ModelConfig,
         enable_overlap: bool,
-        speculative_algorithm: Optional[SpeculativeAlgorithm] = None,
+        spec_algorithm: SpeculativeAlgorithm,
     ):
         return cls(
             reqs=reqs,
@@ -600,7 +600,7 @@ class ScheduleBatch:
             has_stream=any(req.stream for req in reqs),
             has_grammar=any(req.grammar for req in reqs),
             device=req_to_token_pool.device,
-            spec_algorithm=speculative_algorithm,
+            spec_algorithm=spec_algorithm,
         )
 
     def batch_size(self):
@@ -1010,6 +1010,8 @@ class ScheduleBatch:
 
     def prepare_for_decode(self):
         self.forward_mode = ForwardMode.DECODE
+        if self.spec_algorithm.is_eagle():
+            return
 
         self.input_ids = self.output_ids
         self.output_ids = None
@@ -1172,6 +1174,7 @@ class ScheduleBatch:
             out_cache_loc=self.out_cache_loc,
             return_logprob=self.return_logprob,
             decoding_reqs=self.decoding_reqs,
+            spec_algorithm=self.spec_algorithm,
         )
 
     def __str__(self):
@@ -1232,8 +1235,8 @@ class ModelWorkerBatch:
     input_embeds: Optional[torch.tensor] = None
 
     # Speculative decoding
+    spec_algorithm: SpeculativeAlgorithm = None
     spec_info: Optional[SpecInfo] = None
-    spec_algorithm: Optional[SpeculativeAlgorithm] = None
 
 
 @triton.jit

python/sglang/srt/managers/scheduler.py

@@ -76,6 +76,7 @@ from sglang.srt.mem_cache.radix_cache import RadixCache
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
 from sglang.srt.model_executor.forward_batch_info import ForwardMode
 from sglang.srt.server_args import PortArgs, ServerArgs
+from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
     broadcast_pyobj,
     configure_logger,
@@ -116,6 +117,14 @@ class Scheduler:
         self.enable_overlap = not server_args.disable_overlap_schedule
         self.skip_tokenizer_init = server_args.skip_tokenizer_init
         self.enable_metrics = server_args.enable_metrics
+        self.spec_algorithm = SpeculativeAlgorithm.from_string(
+            server_args.speculative_algorithm
+        )
+        self.decode_mem_cache_buf_multiplier = (
+            self.server_args.speculative_num_draft_tokens
+            if not self.spec_algorithm.is_none()
+            else 1
+        )
 
         # Init inter-process communication
         context = zmq.Context(2)
@@ -199,6 +208,21 @@ class Scheduler:
             nccl_port=port_args.nccl_port,
         )
 
+        # Launch worker for speculative decoding if need
+        if self.spec_algorithm.is_eagle():
+            from sglang.srt.speculative.eagle_worker import EAGLEWorker
+
+            self.draft_worker = EAGLEWorker(
+                gpu_id=gpu_id,
+                tp_rank=tp_rank,
+                server_args=server_args,
+                nccl_port=port_args.nccl_port,
+                target_worker=self.tp_worker,
+                dp_rank=dp_rank,
+            )
+        else:
+            self.draft_worker = None
+
         # Get token and memory info from the model worker
         (
             self.max_total_num_tokens,
@@ -855,6 +879,7 @@ class Scheduler:
             self.tree_cache,
             self.model_config,
             self.enable_overlap,
+            self.spec_algorithm,
         )
         new_batch.prepare_for_extend()
 
@@ -888,11 +913,15 @@ class Scheduler:
             return None
 
         # Check if decode out of memory
-        if not batch.check_decode_mem() or (test_retract and batch.batch_size() > 10):
+        if not batch.check_decode_mem(self.decode_mem_cache_buf_multiplier) or (
+            test_retract and batch.batch_size() > 10
+        ):
             old_ratio = self.new_token_ratio
 
             retracted_reqs, new_token_ratio = batch.retract_decode()
             self.new_token_ratio = new_token_ratio
+            if self.draft_worker:
+                self.draft_worker.finish_request(retracted_reqs)
 
             logger.info(
                 "Decode out of memory happened. "
@@ -926,11 +955,17 @@ class Scheduler:
         self.forward_ct += 1
 
         if self.is_generation:
-            model_worker_batch = batch.get_model_worker_batch()
             if batch.forward_mode.is_decode() or batch.extend_num_tokens != 0:
-                logits_output, next_token_ids = self.tp_worker.forward_batch_generation(
-                    model_worker_batch
-                )
+                if self.spec_algorithm.is_none():
+                    model_worker_batch = batch.get_model_worker_batch()
+                    logits_output, next_token_ids = (
+                        self.tp_worker.forward_batch_generation(model_worker_batch)
+                    )
+                else:
+                    logits_output, next_token_ids, model_worker_batch, spec_info = (
+                        self.draft_worker.forward_batch_speculative_generation(batch)
+                    )
+                    batch.spec_info = spec_info
             elif batch.forward_mode.is_idle():
                 model_worker_batch = batch.get_model_worker_batch()
                 self.tp_worker.forward_batch_idle(model_worker_batch)
@@ -1077,7 +1112,10 @@ class Scheduler:
                 self.token_to_kv_pool.free(batch.out_cache_loc[i : i + 1])
                 continue
 
-            req.output_ids.append(next_token_id)
+            if batch.spec_algorithm.is_none():
+                # speculative worker will solve the output_ids in speculative decoding
+                req.output_ids.append(next_token_id)
+
             req.check_finished()
 
             if req.finished():
@@ -1252,6 +1290,9 @@ class Scheduler:
                     # If not stream, we still want to output some tokens to get the benefit of incremental decoding.
                     or (not req.stream and len(req.output_ids) % 50 == 0)
                 ):
+                    if self.draft_worker and req.finished():
+                        self.draft_worker.finish_request(req)
+
                     rids.append(req.rid)
                     finished_reasons.append(
                         req.finished_reason.to_json() if req.finished_reason else None
@@ -1383,6 +1424,7 @@ class Scheduler:
             self.tree_cache,
             self.model_config,
             self.enable_overlap,
+            self.spec_algorithm,
         )
         idle_batch.prepare_for_idle()
         return idle_batch

python/sglang/srt/managers/tp_worker.py

@@ -45,13 +45,18 @@ class TpModelWorker:
         tp_rank: int,
         dp_rank: Optional[int],
         nccl_port: int,
+        is_draft_worker: bool = False,
     ):
         # Parse args
         self.tp_rank = tp_rank
 
         # Init model and tokenizer
         self.model_config = ModelConfig(
-            server_args.model_path,
+            (
+                server_args.model_path
+                if not is_draft_worker
+                else server_args.speculative_draft_model_path
+            ),
             trust_remote_code=server_args.trust_remote_code,
             revision=server_args.revision,
             context_length=server_args.context_length,
@@ -68,6 +73,7 @@ class TpModelWorker:
             tp_size=server_args.tp_size,
             nccl_port=nccl_port,
             server_args=server_args,
+            is_draft_worker=is_draft_worker,
         )
         if server_args.skip_tokenizer_init:
             self.tokenizer = self.processor = None

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -33,7 +33,7 @@ from sglang.srt.model_executor.forward_batch_info import (
     ForwardBatch,
     ForwardMode,
 )
-from sglang.srt.utils import maybe_torch_compile, monkey_patch_vllm_all_gather
+from sglang.srt.utils import monkey_patch_vllm_all_gather
 
 if TYPE_CHECKING:
     from sglang.srt.model_executor.model_runner import ModelRunner
@@ -106,11 +106,6 @@ def set_torch_compile_config():
         torch._dynamo.config.cache_size_limit = 1024
 
 
-@maybe_torch_compile(dynamic=True)
-def clamp_position(seq_lens):
-    return torch.clamp((seq_lens - 1), min=0).to(torch.int64)
-
-
 class CudaGraphRunner:
     """A CudaGraphRunner runs the forward pass of a model with cuda graph and torch.compile."""
 
@@ -157,6 +152,17 @@ class CudaGraphRunner:
         self.capture_forward_mode = ForwardMode.DECODE
         self.num_tokens_per_bs = 1
 
+        if model_runner.spec_algorithm.is_eagle():
+            if self.model_runner.is_draft_worker:
+                self.num_tokens_per_bs = (
+                    self.model_runner.server_args.speculative_eagle_topk
+                )
+            else:
+                self.capture_forward_mode = ForwardMode.TARGET_VERIFY
+                self.num_tokens_per_bs = (
+                    self.model_runner.server_args.speculative_num_draft_tokens
+                )
+
         self.compile_bs = (
             [
                 bs
@@ -192,6 +198,13 @@ class CudaGraphRunner:
             self.positions = torch.zeros((self.max_num_token,), dtype=torch.int64)
             self.mrope_positions = torch.zeros((3, self.max_bs), dtype=torch.int32)
 
+            # Speculative_inference
+            if model_runner.spec_algorithm.is_eagle():
+                self.hidden_states = torch.zeros(
+                    (self.max_num_token, self.model_runner.model_config.hidden_size),
+                    dtype=self.model_runner.dtype,
+                )
+
             if self.is_encoder_decoder:
                 # NOTE: encoder_lens can influence the full_text_row_masked_out_mask tensor when doing mixed batch
                 self.encoder_lens = torch.full(
@@ -234,9 +247,6 @@ class CudaGraphRunner:
             self.model_runner.model.capture_mode = False
 
     def can_run(self, forward_batch: ForwardBatch):
-        if not forward_batch.forward_mode.is_cuda_graph():
-            return False
-
         if self.enable_dp_attention:
             min_num_tokens, max_num_tokens = min(forward_batch.global_num_tokens), max(
                 forward_batch.global_num_tokens
@@ -291,21 +301,18 @@ class CudaGraphRunner:
     def capture_one_batch_size(self, bs: int, forward: Callable):
         graph = torch.cuda.CUDAGraph()
         stream = self.stream
-        num_token = bs * self.num_tokens_per_bs
+        num_tokens = bs * self.num_tokens_per_bs
 
         # Common inputs
-        input_ids = self.input_ids[:num_token]
+        input_ids = self.input_ids[:num_tokens]
         req_pool_indices = self.req_pool_indices[:bs]
         seq_lens = self.seq_lens[:bs]
-        out_cache_loc = self.out_cache_loc[:num_token]
-        positions = self.positions[:num_token]
-
+        out_cache_loc = self.out_cache_loc[:num_tokens]
+        positions = self.positions[:num_tokens]
         if self.is_encoder_decoder:
             encoder_lens = self.encoder_lens[:bs]
         else:
             encoder_lens = None
-
-        seq_lens_sum = seq_lens.sum().item()
         mrope_positions = self.mrope_positions[:, :bs]
 
         if self.enable_dp_attention:
@@ -325,20 +332,22 @@ class CudaGraphRunner:
             token_to_kv_pool=self.model_runner.token_to_kv_pool,
             attn_backend=self.model_runner.attn_backend,
             out_cache_loc=out_cache_loc,
-            seq_lens_sum=seq_lens_sum,
+            seq_lens_sum=seq_lens.sum(),
             encoder_lens=encoder_lens,
             return_logprob=False,
-            top_logprobs_nums=[0] * num_token,
+            top_logprobs_nums=[0] * bs,
             positions=positions,
             global_num_tokens=global_num_tokens,
             mrope_positions=mrope_positions,
             gathered_buffer=gathered_buffer,
+            spec_algorithm=self.model_runner.spec_algorithm,
+            spec_info=self.get_spec_info(num_tokens, positions),
         )
 
         # Attention backend
         self.model_runner.attn_backend.init_forward_metadata_capture_cuda_graph(
             bs,
-            num_token,
+            num_tokens,
             req_pool_indices,
             seq_lens,
             encoder_lens,
@@ -394,14 +403,16 @@ class CudaGraphRunner:
         self.req_pool_indices[:raw_bs].copy_(forward_batch.req_pool_indices)
         self.seq_lens[:raw_bs].copy_(forward_batch.seq_lens)
         self.out_cache_loc[:raw_num_token].copy_(forward_batch.out_cache_loc)
-        positions = clamp_position(forward_batch.seq_lens)
-        self.positions[:raw_num_token].copy_(positions)
+        self.positions[:raw_num_token].copy_(forward_batch.positions)
 
         if self.is_encoder_decoder:
             self.encoder_lens[:raw_bs].copy_(forward_batch.encoder_lens)
         if forward_batch.mrope_positions is not None:
             self.mrope_positions[:, :raw_bs].copy_(forward_batch.mrope_positions)
 
+        if hasattr(forward_batch.spec_info, "hidden_states"):
+            self.hidden_states[:raw_num_token] = forward_batch.spec_info.hidden_states
+
         # Attention backend
         self.model_runner.attn_backend.init_forward_metadata_replay_cuda_graph(
             bs,
@@ -424,3 +435,36 @@ class CudaGraphRunner:
             ),
         )
         return logits_output
+
+    def get_spec_info(self, num_tokens: int, positions: torch.Tensor):
+        spec_info = None
+        if self.model_runner.spec_algorithm.is_eagle():
+            from sglang.srt.speculative.eagle_utils import (
+                EAGLEDraftInput,
+                EagleVerifyInput,
+            )
+
+            if self.model_runner.is_draft_worker:
+                spec_info = EAGLEDraftInput()
+                spec_info.hidden_states = self.hidden_states[:num_tokens]
+                spec_info.positions = positions
+                spec_info.capture_hidden_mode = CaptureHiddenMode.FULL
+                spec_info.init(self.model_runner.server_args)
+            else:
+                spec_info = EagleVerifyInput(
+                    None,
+                    None,
+                    None,
+                    None,
+                    None,
+                    None,
+                    self.model_runner.server_args.speculative_num_draft_tokens,
+                )
+                spec_info.custom_mask = torch.zeros(
+                    (num_tokens * self.model_runner.model_config.context_len),
+                    dtype=torch.bool,
+                    device="cuda",
+                )
+                spec_info.capture_hidden_mode = CaptureHiddenMode.FULL
+
+        return spec_info

python/sglang/srt/model_executor/forward_batch_info.py

@@ -38,6 +38,7 @@ import triton
 import triton.language as tl
 
 from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
+from sglang.srt.utils import maybe_torch_compile
 
 if TYPE_CHECKING:
     from sglang.srt.layers.attention import AttentionBackend
@@ -276,10 +277,21 @@ class ForwardBatch:
             )
 
         if ret.forward_mode.is_idle():
+            ret.positions = torch.empty((0,), device=device)
             return ret
 
+        # Override the positions with spec_info
+        if (
+            ret.spec_info is not None
+            and getattr(ret.spec_info, "positions", None) is not None
+        ):
+            ret.positions = ret.spec_info.positions
+
         # Init position information
-        if not ret.forward_mode.is_decode():
+        if ret.forward_mode.is_decode():
+            if ret.positions is None:
+                ret.positions = clamp_position(batch.seq_lens)
+        else:
             ret.extend_seq_lens = torch.tensor(
                 batch.extend_seq_lens, dtype=torch.int32
             ).to(device, non_blocking=True)
@@ -288,13 +300,15 @@ class ForwardBatch:
             ).to(device, non_blocking=True)
             if model_runner.server_args.attention_backend != "torch_native":
                 ret.extend_num_tokens = batch.extend_num_tokens
-                ret.positions, ret.extend_start_loc = compute_position_triton(
+                positions, ret.extend_start_loc = compute_position_triton(
                     ret.extend_prefix_lens, ret.extend_seq_lens, ret.extend_num_tokens
                 )
             else:
-                ret.positions, ret.extend_start_loc = compute_position_torch(
+                positions, ret.extend_start_loc = compute_position_torch(
                     ret.extend_prefix_lens, ret.extend_seq_lens
                 )
+            if ret.positions is None:
+                ret.positions = positions
             ret.extend_prefix_lens_cpu = batch.extend_prefix_lens
             ret.extend_seq_lens_cpu = batch.extend_seq_lens
             ret.extend_logprob_start_lens_cpu = batch.extend_logprob_start_lens
@@ -383,6 +397,11 @@ def compute_position_torch(
     return positions.to(torch.int64), extend_start_loc
 
 
+@maybe_torch_compile(dynamic=True)
+def clamp_position(seq_lens):
+    return torch.clamp((seq_lens - 1), min=0).to(torch.int64)
+
+
 class CaptureHiddenMode(IntEnum):
     NULL = auto()
     FULL = auto()

python/sglang/srt/model_executor/model_runner.py

@@ -49,6 +49,7 @@ from sglang.srt.mem_cache.memory_pool import (
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader import get_model
 from sglang.srt.server_args import ServerArgs
+from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
     enable_show_time_cost,
     get_available_gpu_memory,
@@ -74,6 +75,7 @@ class ModelRunner:
         tp_size: int,
         nccl_port: int,
         server_args: ServerArgs,
+        is_draft_worker: bool = False,
     ):
         # Parse args
         self.model_config = model_config
@@ -84,8 +86,12 @@ class ModelRunner:
         self.tp_size = tp_size
         self.dist_port = nccl_port
         self.server_args = server_args
+        self.is_draft_worker = is_draft_worker
         self.is_generation = model_config.is_generation
         self.is_multimodal = model_config.is_multimodal
+        self.spec_algorithm = SpeculativeAlgorithm.from_string(
+            server_args.speculative_algorithm
+        )
 
         # Model-specific adjustment
         if (
@@ -205,14 +211,18 @@ class ModelRunner:
         else:
             dist_init_method = f"tcp://127.0.0.1:{self.dist_port}"
         set_custom_all_reduce(not self.server_args.disable_custom_all_reduce)
-        init_distributed_environment(
-            backend=backend,
-            world_size=self.tp_size,
-            rank=self.tp_rank,
-            local_rank=self.gpu_id,
-            distributed_init_method=dist_init_method,
-        )
-        initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
+
+        if not self.is_draft_worker:
+            # Only initilzie the distributed environment on the target model worker.
+            init_distributed_environment(
+                backend=backend,
+                world_size=self.tp_size,
+                rank=self.tp_rank,
+                local_rank=self.gpu_id,
+                distributed_init_method=dist_init_method,
+            )
+            initialize_model_parallel(tensor_model_parallel_size=self.tp_size)
+
         min_per_gpu_memory = get_available_gpu_memory(
             self.device, self.gpu_id, distributed=self.tp_size > 1
         )
@@ -407,7 +417,6 @@ class ModelRunner:
         target_dtype = (
             dtype if isinstance(dtype, torch.dtype) else getattr(torch, dtype)
         )
-        current_dtype = self.dtype if isinstance(self.dtype, str) else self.dtype
 
         assert (
             self._model_update_group is not None
@@ -506,6 +515,28 @@ class ModelRunner:
             )
 
         self.max_total_num_tokens = self.profile_max_num_token(total_gpu_memory)
+
+        if max_num_reqs is None:
+            max_num_reqs = min(
+                max(
+                    int(
+                        self.max_total_num_tokens / self.model_config.context_len * 512
+                    ),
+                    2048,
+                ),
+                4096,
+            )
+
+        if not self.spec_algorithm.is_none():
+            if self.is_draft_worker:
+                self.max_total_num_tokens = self.server_args.draft_runner_cache_size
+            else:
+                self.server_args.draft_runner_cache_size = (
+                    self.max_total_num_tokens
+                    + max_num_reqs * self.server_args.speculative_num_steps
+                    + 100
+                )
+
         if max_total_tokens is not None:
             if max_total_tokens > self.max_total_num_tokens:
                 logging.warning(
@@ -520,17 +551,6 @@ class ModelRunner:
                 "Not enough memory. Please try to increase --mem-fraction-static."
             )
 
-        if max_num_reqs is None:
-            max_num_reqs = min(
-                max(
-                    int(
-                        self.max_total_num_tokens / self.model_config.context_len * 512
-                    ),
-                    2048,
-                ),
-                4096,
-            )
-
         self.req_to_token_pool = ReqToTokenPool(
             size=max_num_reqs + 1,
             max_context_len=self.model_config.context_len + 4,
@@ -650,10 +670,6 @@ class ModelRunner:
         tensor_parallel(self.model, device_mesh)
 
     def forward_decode(self, forward_batch: ForwardBatch):
-        if self.cuda_graph_runner and self.cuda_graph_runner.can_run(forward_batch):
-            return self.cuda_graph_runner.replay(forward_batch)
-
-        forward_batch.positions = (forward_batch.seq_lens - 1).to(torch.int64)
         self.attn_backend.init_forward_metadata(forward_batch)
         return self.model.forward(
             forward_batch.input_ids, forward_batch.positions, forward_batch
@@ -683,14 +699,18 @@ class ModelRunner:
             )
 
     def forward_idle(self, forward_batch: ForwardBatch):
-        if self.cuda_graph_runner and self.cuda_graph_runner.can_run(forward_batch):
-            return self.cuda_graph_runner.replay(forward_batch)
-
         return self.model.forward(
             forward_batch.input_ids, forward_batch.positions, forward_batch
         )
 
     def forward(self, forward_batch: ForwardBatch) -> LogitsProcessorOutput:
+        if (
+            forward_batch.forward_mode.is_cuda_graph()
+            and self.cuda_graph_runner
+            and self.cuda_graph_runner.can_run(forward_batch)
+        ):
+            return self.cuda_graph_runner.replay(forward_batch)
+
         if forward_batch.forward_mode.is_decode():
             return self.forward_decode(forward_batch)
         elif forward_batch.forward_mode.is_extend():

python/sglang/srt/server_args.py

@@ -23,6 +23,7 @@ from typing import List, Optional
 import torch
 
 from sglang.srt.hf_transformers_utils import check_gguf_file
+from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
 from sglang.srt.utils import (
     get_amdgpu_memory_capacity,
     get_hpu_memory_capacity,
@@ -247,6 +248,17 @@ class ServerArgs:
                 "Overlap scheduler is disabled."
             )
 
+        # Speculative Decoding
+        if self.speculative_algorithm == "EAGLE":
+            self.prefill_only_one_req = True
+            self.disable_cuda_graph_padding = True
+            self.disable_radix_cache = True
+            self.disable_overlap_schedule = True
+            self.chunked_prefill_size = -1
+            logger.info(
+                "The radix cache, chunked prefill, and overlap scheduler are disabled because of using eagle speculative decoding."
+            )
+
         # GGUF
         if (
             self.load_format == "auto" or self.load_format == "gguf"

python/sglang/srt/speculative/spec_info.py

@@ -2,8 +2,12 @@ from enum import IntEnum, auto
 
 
 class SpeculativeAlgorithm(IntEnum):
+    NONE = auto()
     EAGLE = auto()
 
+    def is_none(self):
+        return self == SpeculativeAlgorithm.NONE
+
     def is_eagle(self):
         return self == SpeculativeAlgorithm.EAGLE
 
@@ -11,6 +15,7 @@ class SpeculativeAlgorithm(IntEnum):
     def from_string(name: str):
         name_map = {
             "EAGLE": SpeculativeAlgorithm.EAGLE,
+            None: SpeculativeAlgorithm.NONE,
         }
         return name_map[name]