Merge branch 'main' into cheng/refactor/sbo

Author: ishandhanani

python/sglang/srt/lora/backend/base_backend.py

@@ -1,8 +1,7 @@
-from typing import Optional, Tuple, Union
+from typing import Tuple, Union
 
 import torch
 
-from sglang.srt.lora.utils import LoRABatchInfo
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 
 
@@ -97,8 +96,8 @@ def run_gate_up_lora(
 
     def init_cuda_graph_batch_info(
         self,
-        cuda_graph_batch_info: LoRABatchInfo,
         max_bs_in_cuda_graph: int,
+        num_tokens_per_bs: int,
     ):
         """Initialize the batch info for CUDA Graph mode.
 
@@ -108,6 +107,7 @@ def init_cuda_graph_batch_info(
         Args:
             cuda_graph_batch_info: the LoRABatchInfo object created in LoraManager
             max_bs_in_cuda_graph: maximum batch size for CUDA Graph mode
+            num_tokens_per_bs: number of tokens per sequence (1 for decoding, >1 for target_verify)
         """
         pass
 
@@ -117,7 +117,7 @@ def prepare_lora_batch(
         weight_indices: list[int],
         lora_ranks: list[int],
         scalings: list[float],
-        batch_info: Optional[LoRABatchInfo] = None,
+        use_cuda_graph: bool,
     ):
         """Prepare the lora weights and batch info for current forward batch.
 
@@ -129,7 +129,6 @@ def prepare_lora_batch(
             weight_indices: list of indices of lora weights to be applied for current batch
             lora_ranks: list of lora ranks corresponding to weight_indices
             scalings: list of scaling factors corresponding to weight_indices
-            batch_info: optional LoRABatchInfo object, if not provided, the backend should use its own
-                        internal batch info (e.g., self.cuda_graph_batch_info for CUDA Graph mode)
+            use_cuda_graph: whether to use CUDA Graph for this batch
         """
         pass

python/sglang/srt/lora/backend/chunked_backend.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import torch
 
 from sglang.srt.lora.backend.base_backend import BaseLoRABackend
@@ -52,7 +50,7 @@ def run_lora_b_sgemm(
         output_offset: torch.Tensor,
         base_output: torch.Tensor = None,
         *args,
-        **kwargs
+        **kwargs,
     ) -> torch.Tensor:
         # For simple lora B, we use slice offsets [0, output_dim]
         output_dim = weights.shape[-2]
@@ -75,7 +73,7 @@ def run_qkv_lora(
         max_qkv_out_dim: int,
         base_output: torch.Tensor = None,
         *args,
-        **kwargs
+        **kwargs,
     ) -> torch.Tensor:
 
         # x: (s, input_dim)
@@ -107,7 +105,7 @@ def run_gate_up_lora(
         output_offset: torch.Tensor,
         base_output: torch.Tensor = None,
         *args,
-        **kwargs
+        **kwargs,
     ) -> torch.Tensor:
 
         # x: (s, input_dim)
@@ -160,13 +158,36 @@ def _determine_chunk_size(self, forward_batch: ForwardBatch) -> int:
             chunk_size = 16
         return min(self.max_chunk_size, chunk_size)
 
+    def init_cuda_graph_batch_info(
+        self,
+        max_bs_in_cuda_graph: int,
+        num_tokens_per_bs: int,
+    ):
+        max_num_segments = (
+            (num_tokens_per_bs + MIN_CHUNK_SIZE - 1) // MIN_CHUNK_SIZE
+        ) * max_bs_in_cuda_graph
+        max_num_tokens = max_bs_in_cuda_graph * num_tokens_per_bs
+        with torch.device("cuda"):
+            self.cuda_graph_batch_info = LoRABatchInfo(
+                bs=max_bs_in_cuda_graph,
+                use_cuda_graph=True,
+                seg_lens=torch.zeros(max_num_segments, dtype=torch.int32),
+                seg_indptr=torch.zeros(max_num_segments + 1, dtype=torch.int32),
+                weight_indices=torch.zeros(max_num_segments, dtype=torch.int32),
+                permutation=torch.zeros(max_num_tokens, dtype=torch.int32),
+                lora_ranks=torch.zeros(self.max_loras_per_batch, dtype=torch.int32),
+                scalings=torch.zeros(self.max_loras_per_batch, dtype=torch.float),
+                num_segments=None,  # Set per batch
+                max_len=None,  # Not used in CSGMV backend
+            )
+
     def prepare_lora_batch(
         self,
         forward_batch: ForwardBatch,
         weight_indices: list[int],
         lora_ranks: list[int],
         scalings: list[float],
-        batch_info: Optional[LoRABatchInfo] = None,
+        use_cuda_graph: bool,
     ):
         chunk_size = self._determine_chunk_size(forward_batch)
 
@@ -188,7 +209,7 @@ def prepare_lora_batch(
             scalings, dtype=torch.float, pin_memory=True, device="cpu"
         )
 
-        if batch_info is None:
+        if not use_cuda_graph:
             batch_info = LoRABatchInfo(
                 bs=forward_batch.batch_size,
                 num_segments=num_segments,
@@ -213,6 +234,7 @@ def prepare_lora_batch(
                 seg_lens=None,
             )
         else:
+            batch_info = self.cuda_graph_batch_info
             batch_info.bs = forward_batch.batch_size
             batch_info.num_segments = num_segments
             batch_info.max_len = chunk_size
@@ -262,14 +284,23 @@ def _get_permutation(seq_weight_indices, forward_batch: ForwardBatch):
         with torch.device("cpu"):
             seq_weight_indices = torch.tensor(seq_weight_indices, dtype=torch.int32)
 
-            seg_lens_cpu = (
-                torch.tensor(
+            if forward_batch.forward_mode.is_decode():
+                seg_lens_cpu = torch.ones(forward_batch.batch_size, dtype=torch.int32)
+            elif forward_batch.forward_mode.is_target_verify():
+                seg_lens_cpu = torch.full(
+                    size=(forward_batch.batch_size,),
+                    fill_value=forward_batch.spec_info.draft_token_num,
+                    dtype=torch.int32,
+                )
+            elif forward_batch.forward_mode.is_extend():
+                seg_lens_cpu = torch.tensor(
                     forward_batch.extend_seq_lens_cpu,
                     dtype=torch.int32,
                 )
-                if forward_batch.forward_mode.is_extend()
-                else torch.ones(forward_batch.batch_size, dtype=torch.int32)
-            )
+            else:
+                raise ValueError(
+                    f"Unsupported forward mode: {forward_batch.forward_mode}"
+                )
 
             row_weight_indices = torch.repeat_interleave(
                 seq_weight_indices, seg_lens_cpu

python/sglang/srt/lora/backend/triton_backend.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import torch
 
 from sglang.srt.lora.backend.base_backend import BaseLoRABackend
@@ -97,24 +95,41 @@ def run_gate_up_lora(
         return lora_output
 
     def init_cuda_graph_batch_info(
-        self, cuda_graph_batch_info: LoRABatchInfo, max_bs_in_cuda_graph: int
+        self,
+        max_bs_in_cuda_graph: int,
+        num_tokens_per_bs: int,
     ):
-        # Initialize seg_lens and seg_indptr for CUDA graph as they remain constant
-        # across batches.
-        cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph].fill_(1)
-        torch.cumsum(
-            cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph],
-            dim=0,
-            out=cuda_graph_batch_info.seg_indptr[1 : max_bs_in_cuda_graph + 1],
-        )
+        with torch.device("cuda"):
+            self.cuda_graph_batch_info = LoRABatchInfo(
+                bs=max_bs_in_cuda_graph,
+                use_cuda_graph=True,
+                num_segments=None,
+                seg_lens=torch.full(
+                    (max_bs_in_cuda_graph,), num_tokens_per_bs, dtype=torch.int32
+                ),
+                seg_indptr=torch.empty(max_bs_in_cuda_graph + 1, dtype=torch.int32),
+                max_len=num_tokens_per_bs,
+                weight_indices=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
+                lora_ranks=torch.zeros(self.max_loras_per_batch, dtype=torch.int32),
+                scalings=torch.zeros(self.max_loras_per_batch, dtype=torch.float),
+                permutation=None,
+            )
+
+            # Initialize seg_indptr for CUDA graph as they remain constant
+            # across batches.
+            torch.cumsum(
+                self.cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph],
+                dim=0,
+                out=self.cuda_graph_batch_info.seg_indptr[1 : max_bs_in_cuda_graph + 1],
+            )
 
     def prepare_lora_batch(
         self,
         forward_batch: ForwardBatch,
         weight_indices: list[int],
         lora_ranks: list[int],
         scalings: list[float],
-        batch_info: Optional[LoRABatchInfo] = None,
+        use_cuda_graph: bool,
     ):
         # Use pinned memory to avoid synchronizations during host-to-device transfer
         weight_indices_tensor = torch.tensor(
@@ -129,10 +144,11 @@ def prepare_lora_batch(
 
         bs = forward_batch.batch_size
 
-        if batch_info is not None:
+        if use_cuda_graph:
             assert (
-                batch_info.use_cuda_graph
-            ), "batch_info.use_cuda_graph must be True when batch_info is provided"
+                self.cuda_graph_batch_info is not None
+            ), "CUDA Graph batch info is not initialized."
+            batch_info = self.cuda_graph_batch_info
             batch_info.bs = forward_batch.batch_size
             batch_info.num_segments = forward_batch.batch_size
         else:

python/sglang/srt/lora/lora_manager.py

@@ -29,7 +29,6 @@
 from sglang.srt.lora.lora_registry import LoRARef
 from sglang.srt.lora.mem_pool import LoRAMemoryPool
 from sglang.srt.lora.utils import (
-    LoRABatchInfo,
     LoRAType,
     get_layer_id,
     get_normalized_target_modules,
@@ -95,25 +94,13 @@ def __init__(
             lora_paths=lora_paths,
         )
 
-    def init_cuda_graph_batch_info(self, max_bs_in_cuda_graph: int):
+    def init_cuda_graph_batch_info(
+        self, max_bs_in_cuda_graph: int, num_tokens_per_bs: int
+    ):
         self.max_bs_in_cuda_graph = max_bs_in_cuda_graph
-        with torch.device("cuda"):
-            self.cuda_graph_batch_info = LoRABatchInfo(
-                bs=max_bs_in_cuda_graph,
-                use_cuda_graph=True,
-                num_segments=None,
-                seg_lens=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
-                seg_indptr=torch.zeros(max_bs_in_cuda_graph + 1, dtype=torch.int32),
-                max_len=1,
-                weight_indices=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
-                permutation=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
-                lora_ranks=torch.zeros(self.max_loras_per_batch, dtype=torch.int32),
-                scalings=torch.zeros(self.max_loras_per_batch, dtype=torch.float),
-            )
-
         self.lora_backend.init_cuda_graph_batch_info(
-            cuda_graph_batch_info=self.cuda_graph_batch_info,
             max_bs_in_cuda_graph=max_bs_in_cuda_graph,
+            num_tokens_per_bs=num_tokens_per_bs,
         )
 
     def create_lora_update_result(
@@ -297,7 +284,7 @@ def prepare_lora_batch(self, forward_batch: ForwardBatch):
             weight_indices=weight_indices,
             lora_ranks=lora_ranks,
             scalings=scalings,
-            batch_info=self.cuda_graph_batch_info if use_cuda_graph else None,
+            use_cuda_graph=use_cuda_graph,
         )
 
     def update_lora_info(self):

python/sglang/srt/lora/utils.py

@@ -19,9 +19,6 @@ class LoRABatchInfo:
     # Number of segments. For triton backend, it is equal to batch size.
     num_segments: int
 
-    # Maximum segment length of current batch
-    max_len: int
-
     # Indice pointers of each segment in shape (num_segments + 1, )
     seg_indptr: torch.Tensor
 
@@ -34,6 +31,9 @@ class LoRABatchInfo:
     # scaling of each lora adapter, in shape (lora_num,)
     scalings: torch.Tensor
 
+    # Maximum segment length of current batch
+    max_len: Optional[int]
+
     # Lengths of each segments in shape (num_segments,)
     seg_lens: Optional[torch.Tensor]
 

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -308,7 +308,10 @@ def __init__(self, model_runner: ModelRunner):
             set_torch_compile_config()
 
         if self.model_runner.server_args.enable_lora:
-            self.model_runner.lora_manager.init_cuda_graph_batch_info(self.max_bs)
+            self.model_runner.lora_manager.init_cuda_graph_batch_info(
+                max_bs_in_cuda_graph=self.max_bs,
+                num_tokens_per_bs=self.num_tokens_per_bs,
+            )
 
         # Graph inputs
         with torch.device(self.device):

python/sglang/srt/server_args.py

@@ -3874,6 +3874,13 @@ def check_lora_server_args(self):
                 )
 
         if self.enable_lora:
+            # Validate compatibility with speculative decoding
+            if self.speculative_algorithm not in ["NGRAM", None]:
+                raise ValueError(
+                    "Currently LoRA is only compatible with NGRAM speculative decoding."
+                )
+
+            # Parse lora_paths
             if isinstance(self.lora_paths, list):
                 lora_paths = self.lora_paths
                 self.lora_paths = []

python/sglang/test/runners.py

@@ -528,6 +528,8 @@ def __init__(
         speculative_num_steps: Optional[int] = None,
         speculative_eagle_topk: Optional[int] = None,
         speculative_num_draft_tokens: Optional[int] = None,
+        speculative_ngram_min_match_window_size: Optional[int] = None,
+        speculative_ngram_max_match_window_size: Optional[int] = None,
         disable_overlap_schedule: bool = False,
         disable_custom_all_reduce: bool = False,
         torchao_config: Optional[str] = None,
@@ -539,6 +541,7 @@ def __init__(
         max_loaded_loras: Optional[int] = None,
         json_model_override_args: Optional[dict[str, Any]] = None,
         lora_eviction_policy: str = "lru",
+        enable_deterministic_inference: bool = False,
     ):
         self.model_type = model_type
         self.is_generation = model_type == "generation"
@@ -554,6 +557,14 @@ def __init__(
             spec_kwargs["speculative_num_steps"] = speculative_num_steps
             spec_kwargs["speculative_eagle_topk"] = speculative_eagle_topk
             spec_kwargs["speculative_num_draft_tokens"] = speculative_num_draft_tokens
+        elif speculative_algorithm == "NGRAM":
+            spec_kwargs["speculative_algorithm"] = speculative_algorithm
+            spec_kwargs["speculative_ngram_min_match_window_size"] = (
+                speculative_ngram_min_match_window_size
+            )
+            spec_kwargs["speculative_ngram_max_match_window_size"] = (
+                speculative_ngram_max_match_window_size
+            )
 
         self.engine = Engine(
             model_path=model_path,
@@ -594,6 +605,7 @@ def __init__(
                 else "{}"
             ),
             lora_eviction_policy=lora_eviction_policy,
+            enable_deterministic_inference=enable_deterministic_inference,
             **spec_kwargs,
         )