[Fix] avoid stream sync and torch compile in prefill for fa3 backend

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

@@ -79,7 +79,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
             torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)
         )
         # Precompute maximum sequence length
-        metadata.max_seq_len_k = seqlens_in_batch.max().item()
+        metadata.max_seq_len_k = forward_batch.seq_lens_cpu.max().item()
         # Precompute page table
         metadata.page_table = forward_batch.req_to_token_pool.req_to_token[
             forward_batch.req_pool_indices, : metadata.max_seq_len_k

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

@@ -797,7 +797,7 @@ def call_fn(i, forward_batch):
                 encoder_lens=None,
                 forward_mode=ForwardMode.DECODE,
                 spec_info=forward_batch.spec_info,
-                seq_lens_cpu=forward_batch.decode_seq_lens_cpu,
+                seq_lens_cpu=forward_batch.seq_lens_cpu,
             )
 
         self.common_template(forward_batch, self.cuda_graph_kv_indices, call_fn)

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

@@ -92,7 +92,7 @@ def init_forward_metadata(self, forward_batch: ForwardBatch):
         if forward_batch.forward_mode.is_decode_or_idle():
             if spec_info is None:
                 max_seqlen_pad = triton.cdiv(
-                    forward_batch.decode_seq_lens_cpu.max().item(), PAGE_SIZE
+                    forward_batch.seq_lens_cpu.max().item(), PAGE_SIZE
                 )
                 block_kv_indices = torch.full(
                     (bs, max_seqlen_pad),

python/sglang/srt/managers/schedule_batch.py

@@ -1373,21 +1373,22 @@ def merge_batch(self, other: "ScheduleBatch"):
 
     def get_model_worker_batch(self) -> ModelWorkerBatch:
         if self.forward_mode.is_decode_or_idle():
-            if (
-                global_server_args_dict["enable_flashinfer_mla"]
-                or global_server_args_dict["enable_flashmla"]
-                or global_server_args_dict["attention_backend"] == "fa3"
-            ):
-                decode_seq_lens = self.seq_lens.cpu()
-            else:
-                decode_seq_lens = None
             extend_seq_lens = extend_prefix_lens = extend_logprob_start_lens = None
         else:
-            decode_seq_lens = None
             extend_seq_lens = self.extend_lens
             extend_prefix_lens = self.prefix_lens
             extend_logprob_start_lens = self.extend_logprob_start_lens
 
+        # Create seq_lens_cpu when needed
+        if (
+            global_server_args_dict["enable_flashinfer_mla"]
+            or global_server_args_dict["enable_flashmla"]
+            or global_server_args_dict["attention_backend"] == "fa3"
+        ):
+            seq_lens_cpu = self.seq_lens.cpu()
+        else:
+            seq_lens_cpu = None
+
         if self.sampling_info:
             if self.has_grammar:
                 self.sampling_info.grammars = [req.grammar for req in self.reqs]
@@ -1410,7 +1411,7 @@ def get_model_worker_batch(self) -> ModelWorkerBatch:
             global_num_tokens=self.global_num_tokens,
             global_num_tokens_for_logprob=self.global_num_tokens_for_logprob,
             can_run_dp_cuda_graph=self.can_run_dp_cuda_graph,
-            decode_seq_lens=decode_seq_lens,
+            seq_lens_cpu=seq_lens_cpu,
             extend_num_tokens=self.extend_num_tokens,
             extend_seq_lens=extend_seq_lens,
             extend_prefix_lens=extend_prefix_lens,
@@ -1471,6 +1472,7 @@ class ModelWorkerBatch:
     req_pool_indices: torch.Tensor
     # The sequence length
     seq_lens: torch.Tensor
+    seq_lens_cpu: Optional[torch.Tensor]
     # The indices of output tokens in the token_to_kv_pool_allocator
     out_cache_loc: torch.Tensor
 
@@ -1487,9 +1489,6 @@ class ModelWorkerBatch:
     global_num_tokens_for_logprob: Optional[List[int]]
     can_run_dp_cuda_graph: bool
 
-    # For decode
-    decode_seq_lens: Optional[torch.Tensor]
-
     # For extend
     extend_num_tokens: Optional[int]
     extend_seq_lens: Optional[List[int]]

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -489,10 +489,10 @@ def replay_prepare(self, forward_batch: ForwardBatch):
         self.seq_lens[:raw_bs].copy_(forward_batch.seq_lens)
         self.out_cache_loc[:raw_num_token].copy_(forward_batch.out_cache_loc)
         self.positions[:raw_num_token].copy_(forward_batch.positions)
-        if forward_batch.decode_seq_lens_cpu is not None:
+        if forward_batch.seq_lens_cpu is not None:
             if bs != raw_bs:
                 self.seq_lens_cpu.fill_(1)
-            self.seq_lens_cpu[:raw_bs].copy_(forward_batch.decode_seq_lens_cpu)
+            self.seq_lens_cpu[:raw_bs].copy_(forward_batch.seq_lens_cpu)
 
         if self.is_encoder_decoder:
             self.encoder_lens[:raw_bs].copy_(forward_batch.encoder_lens)

python/sglang/srt/model_executor/forward_batch_info.py

@@ -39,7 +39,6 @@
 import triton.language as tl
 
 from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
-from sglang.srt.utils import get_compiler_backend
 
 if TYPE_CHECKING:
     from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
@@ -148,6 +147,9 @@ class ForwardBatch:
     # The sum of all sequence lengths
     seq_lens_sum: int
 
+    # Optional seq_lens on cpu
+    seq_lens_cpu: Optional[torch.Tensor] = None
+
     # For logprob
     return_logprob: bool = False
     top_logprobs_nums: Optional[List[int]] = None
@@ -162,9 +164,6 @@ class ForwardBatch:
     # Position information
     positions: torch.Tensor = None
 
-    # For decode
-    decode_seq_lens_cpu: Optional[torch.Tensor] = None
-
     # For extend
     extend_num_tokens: Optional[int] = None
     extend_seq_lens: Optional[torch.Tensor] = None
@@ -293,12 +292,14 @@ def init_new(
         ):
             ret.positions = ret.spec_info.positions
 
+        # Get seq_lens_cpu if needed
+        if ret.seq_lens_cpu is None:
+            ret.seq_lens_cpu = batch.seq_lens_cpu
+
         # Init position information
         if ret.forward_mode.is_decode():
             if ret.positions is None:
-                ret.positions = clamp_position(batch.seq_lens)
-            if ret.decode_seq_lens_cpu is None:
-                ret.decode_seq_lens_cpu = batch.decode_seq_lens
+                ret.positions = torch.clamp((batch.seq_lens - 1), min=0).to(torch.int64)
         else:
             ret.extend_seq_lens = torch.tensor(
                 batch.extend_seq_lens, dtype=torch.int32
@@ -518,8 +519,3 @@ def compute_position_torch(
     extend_start_loc = torch.zeros_like(extend_seq_lens)
     extend_start_loc[1:] = torch.cumsum(extend_seq_lens[:-1], dim=0)
     return positions.to(torch.int64), extend_start_loc
-
-
-@torch.compile(dynamic=True, backend=get_compiler_backend())
-def clamp_position(seq_lens):
-    return torch.clamp((seq_lens - 1), min=0).to(torch.int64)

python/sglang/srt/speculative/eagle_draft_cuda_graph_runner.py

@@ -214,10 +214,10 @@ def replay(self, forward_batch: ForwardBatch):
             forward_batch.positions = self.positions[:num_tokens]
 
         # Special handle for seq_len_cpu used when flashinfer mla is used
-        if (forward_batch.decode_seq_lens_cpu is not None) and (bs != raw_bs):
+        if (forward_batch.seq_lens_cpu is not None) and (bs != raw_bs):
             self.seq_lens_cpu.fill_(1)
-            self.seq_lens_cpu[:raw_bs].copy_(forward_batch.decode_seq_lens_cpu)
-            forward_batch.decode_seq_lens_cpu = self.seq_lens_cpu[:bs]
+            self.seq_lens_cpu[:raw_bs].copy_(forward_batch.seq_lens_cpu)
+            forward_batch.seq_lens_cpu = self.seq_lens_cpu[:bs]
 
         self.model_runner.draft_attn_backend.init_forward_metadata_replay_cuda_graph(
             forward_batch, bs
@@ -233,7 +233,7 @@ def replay(self, forward_batch: ForwardBatch):
             forward_batch.positions = self.positions[:raw_num_token]
             forward_batch.seq_lens = self.seq_lens[:raw_bs]
             forward_batch.req_pool_indices = self.req_pool_indices[:raw_bs]
-            if forward_batch.decode_seq_lens_cpu is not None:
-                forward_batch.decode_seq_lens_cpu = self.seq_lens_cpu[:raw_bs]
+            if forward_batch.seq_lens_cpu is not None:
+                forward_batch.seq_lens_cpu = self.seq_lens_cpu[:raw_bs]
 
         return out