Fix ngram spec with page size > 1 (#11135)

python/sglang/srt/managers/schedule_batch.py

@@ -1229,7 +1229,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int64).to(
             self.device, non_blocking=True
         )
-        seq_lens_cpu_tensor = torch.tensor(seq_lens, dtype=torch.int64)
+        seq_lens_cpu = torch.tensor(seq_lens, dtype=torch.int64)
         orig_seq_lens_tensor = torch.tensor(orig_seq_lens, dtype=torch.int32).to(
             self.device, non_blocking=True
         )
@@ -1366,7 +1366,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                 prefix_lens_tensor,
                 prefix_lens_cpu_tensor,
                 seq_lens_tensor,
-                seq_lens_cpu_tensor,
+                seq_lens_cpu,
                 last_loc,
                 extend_num_tokens,
             )
@@ -1375,7 +1375,7 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
         self.input_ids = input_ids_tensor
         self.req_pool_indices = req_pool_indices_tensor
         self.seq_lens = seq_lens_tensor
-        self.seq_lens_cpu = seq_lens_cpu_tensor
+        self.seq_lens_cpu = seq_lens_cpu
         self.orig_seq_lens = orig_seq_lens_tensor
         self.out_cache_loc = out_cache_loc
         self.input_embeds = (

python/sglang/srt/server_args.py

@@ -1087,7 +1087,10 @@ class ServerArgs:
                 and self.attention_backend != "flashinfer"
             ):
                 raise ValueError(
-                    "speculative_eagle_topk > 1 with page_size > 1 is unstable and produces incorrect results for paged attention backends. This combination is only supported for the 'flashinfer' backend."
+                    f"speculative_eagle_topk({self.speculative_eagle_topk}) > 1 "
+                    f"with page_size({self.page_size}) > 1 is unstable "
+                    "and produces incorrect results for paged attention backends. "
+                    "This combination is only supported for the 'flashinfer' backend."
                 )
             if self.enable_dp_attention:
                 # TODO: support dp attention for ngram speculative decoding

python/sglang/srt/speculative/eagle_info.py

@@ -388,6 +388,8 @@ class EagleVerifyInput(SpecInput):
         evict_mask = torch.full_like(self.draft_token, True, dtype=torch.bool)
         evict_mask[accept_index] = False
         accept_length_cpu = accept_length.cpu()
+        # FIXME: this `tolist()` fixes the numerical calculation consistency
+        # try to unify the tensor representation and list representation
         accept_length_list = accept_length_cpu.tolist()
 
         if page_size == 1:

python/sglang/srt/speculative/ngram_utils.py

@@ -79,14 +79,21 @@ class NgramVerifyInput(SpecInput):
         else:
             # TODO(lsyin): add prefix lens cpu here to support page size > 1
             prefix_lens = batch.seq_lens
+            prefix_lens_cpu = batch.seq_lens_cpu
             end_offset = prefix_lens + self.draft_token_num
+            end_offset_cpu = prefix_lens_cpu + self.draft_token_num
             last_loc = get_last_loc(
                 batch.req_to_token_pool.req_to_token,
                 batch.req_pool_indices,
                 prefix_lens,
             )
             batch.out_cache_loc = batch.alloc_paged_token_slots_extend(
-                prefix_lens, end_offset, last_loc, len(batch.input_ids)
+                prefix_lens,
+                prefix_lens_cpu,
+                end_offset,
+                end_offset_cpu,
+                last_loc,
+                len(batch.input_ids),
             )
             self.last_loc = last_loc