[overlap-spec] Make plan stream an option (#11724)

python/sglang/srt/environ.py

@@ -221,6 +221,9 @@ class Envs:
     SGLANG_TRITON_PREFILL_TRUNCATION_ALIGN_SIZE = EnvInt(4096)
     SGLANG_TRITON_DECODE_SPLIT_TILE_SIZE = EnvInt(256)
 
+    # Overlap Spec V2
+    SGLANG_ENABLE_OVERLAP_PLAN_STREAM = EnvBool(False)
+
     # VLM
     SGLANG_IMAGE_MAX_PIXELS = EnvInt(16384 * 28 * 28)
     SGLANG_RESIZE_RESAMPLE = EnvStr("")

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

@@ -365,7 +365,7 @@ class TritonAttnBackend(AttentionBackend):
             )
             kv_indptr = kv_indptr[: bs + 1]
             kv_indices = torch.empty(
-                forward_batch.extend_prefix_lens.sum().item(),
+                sum(forward_batch.extend_prefix_lens_cpu),
                 dtype=torch.int64,
                 device=self.device,
             )

python/sglang/srt/model_executor/forward_batch_info.py

@@ -404,6 +404,8 @@ class ForwardBatch:
             if ret.positions is None:
                 ret.positions = clamp_position(batch.seq_lens)
         else:
+            assert isinstance(batch.extend_seq_lens, list)
+            assert isinstance(batch.extend_prefix_lens, list)
             ret.extend_seq_lens = torch.tensor(
                 batch.extend_seq_lens, dtype=torch.int32
             ).to(device, non_blocking=True)

python/sglang/srt/speculative/eagle_info_v2.py

@@ -114,7 +114,7 @@ class EagleDraftInputV2Mixin:
         num_draft_tokens: int,
         draft_model_runner: Any,
     ):
-        seq_lens_cpu_backup = batch.seq_lens_cpu
+        seq_lens_cpu_ = batch.seq_lens_cpu
         extend_num_tokens = len(batch.seq_lens) * num_draft_tokens
 
         batch.spec_info = self
@@ -123,8 +123,7 @@ class EagleDraftInputV2Mixin:
         batch.seq_lens_cpu = batch.seq_lens_cpu + num_draft_tokens
         batch.seq_lens_sum += extend_num_tokens
         batch.extend_seq_lens = [num_draft_tokens for _ in range(len(batch.seq_lens))]
-        batch.extend_prefix_lens = seq_lens_cpu_backup.tolist()
-        batch.extend_prefix_lens_cpu = seq_lens_cpu_backup
+        batch.extend_prefix_lens = seq_lens_cpu_.tolist()
         batch.extend_num_tokens = extend_num_tokens
         batch.capture_hidden_mode = CaptureHiddenMode.FULL
         batch.forward_mode = ForwardMode.DRAFT_EXTEND_V2

python/sglang/srt/speculative/eagle_worker_v2.py

+import contextlib
 import logging
 from typing import List, Optional
 
 import torch
 from torch.cuda import Stream as CudaStream
 
+from sglang.srt.environ import envs
 from sglang.srt.layers.logits_processor import LogitsProcessorOutput
 from sglang.srt.managers.schedule_batch import ModelWorkerBatch, Req
 from sglang.srt.managers.scheduler import GenerationBatchResult
@@ -50,9 +52,13 @@ class EAGLEWorkerV2(EAGLEWorker):
             self.speculative_num_steps * self.topk, self.speculative_num_draft_tokens
         )
         self.tree_mask_mode = TreeMaskMode.FULL_MASK
-        self.plan_stream: CudaStream = torch.get_device_module(self.device).Stream()
-        # TODO(lsyin): potential bugs with a separate plan stream
-        self.plan_stream_ctx = torch.cuda.stream(self.plan_stream)
+
+        if envs.SGLANG_ENABLE_OVERLAP_PLAN_STREAM.get():
+            self.plan_stream: CudaStream = torch.get_device_module(self.device).Stream()
+            self.plan_stream_ctx = torch.cuda.stream(self.plan_stream)
+        else:
+            self.plan_stream = None
+            self.plan_stream_ctx = contextlib.nullcontext()
 
     def forward_batch_generation(self, model_worker_batch: ModelWorkerBatch):
         if model_worker_batch.forward_mode.is_decode():
@@ -232,9 +238,13 @@ class EAGLEWorkerV2(EAGLEWorker):
         batch: ModelWorkerBatch,
         pre_draft_allocate_lens: torch.Tensor,
     ):
+        # Since batch.seq_lens is allocated in another stream, we need
+        # record_stream() to prevent pytorch gc and reuse the gpu memory
+        # while forward_stream is still running.
+        batch.seq_lens.record_stream(torch.cuda.current_stream())
+
         # Parse args
         verify_input: EagleVerifyInput = batch.spec_info
-        seq_lens_backup = batch.seq_lens
         bs = len(batch.seq_lens)
 
         # Batch 1: Target verify
@@ -280,17 +290,8 @@ class EAGLEWorkerV2(EAGLEWorker):
             accept_length,
             accept_index,
         ) = verify_input.sample(batch, logits_output)
-        new_seq_lens = seq_lens_backup + accept_length
+        new_seq_lens = batch.seq_lens + accept_length
         verify_done = torch.cuda.Event()
-
-        # Move the accepted tokens to the target KV cache locations
-        batch.seq_lens = seq_lens_backup
-        self.move_accepted_tokens_to_target_kvcache(
-            batch,
-            accept_index,
-            accept_length,
-        )
-
         verify_done.record()
 
         all_verified_id = predict[accept_index]
@@ -341,11 +342,6 @@ class EAGLEWorkerV2(EAGLEWorker):
         ret_topk_p, ret_topk_index = fast_topk(probs, self.topk, dim=-1)
         ret_hidden_states = draft_logits_output.hidden_states
 
-        # Since seq_lens_backup's tensor is allocated in another stream, we
-        # need record_stream() to prevent pytorch gc and reuse the gpu memory
-        # while forward_stream is still running.
-        seq_lens_backup.record_stream(torch.cuda.current_stream())
-
         # Construct the return values
         next_draft_input = EagleDraftInput(
             topk_p=ret_topk_p,