Support target model verification in the attention backend (#2678)

Co-authored-by: yukavio <kavioyu@gmail.com>

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

@@ -3,7 +3,6 @@ from __future__ import annotations
 from typing import TYPE_CHECKING
 
 import torch
-import torch.nn as nn
 
 from sglang.srt.layers.attention import AttentionBackend
 from sglang.srt.managers.schedule_batch import global_server_args_dict
@@ -52,8 +51,6 @@ class DoubleSparseAttnBackend(AttentionBackend):
 
         self.forward_metadata = None
 
-        self.cuda_graph_max_seq_len = model_runner.model_config.context_len
-
     def init_forward_metadata(self, forward_batch: ForwardBatch):
         """Init auxiliary variables for triton attention backend."""
 
@@ -115,55 +112,6 @@ class DoubleSparseAttnBackend(AttentionBackend):
             ds_req_to_token,
         )
 
-    def init_cuda_graph_state(self, max_bs: int):
-        # TODO(Andy): Support CUDA graph for double sparse attention
-        raise ValueError(
-            "Double sparse attention does not support CUDA graph for now. Please --disable-cuda-graph"
-        )
-        self.cuda_graph_max_total_num_tokens = max_bs * self.cuda_graph_max_seq_len
-
-        self.cuda_graph_start_loc = torch.zeros(
-            (max_bs,), dtype=torch.int32, device="cuda"
-        )
-        self.cuda_graph_attn_logits = torch.empty(
-            (
-                self.num_head,
-                self.cuda_graph_max_total_num_tokens,
-            ),
-            dtype=self.reduce_dtype,
-            device="cuda",
-        )
-
-    def init_forward_metadata_capture_cuda_graph(
-        self,
-        bs: int,
-        req_pool_indices: torch.Tensor,
-        seq_lens: torch.Tensor,
-        encoder_lens=None,
-    ):
-        # NOTE: encoder_lens expected to be zeros or None
-        self.forward_metadata = (
-            self.cuda_graph_start_loc,
-            self.cuda_graph_attn_logits,
-            self.cuda_graph_max_seq_len,
-            None,
-        )
-
-    def init_forward_metadata_replay_cuda_graph(
-        self,
-        bs: int,
-        req_pool_indices: torch.Tensor,
-        seq_lens: torch.Tensor,
-        seq_lens_sum: int,
-        encoder_lens=None,
-    ):
-        # NOTE: encoder_lens expected to be zeros or None
-        self.cuda_graph_start_loc.zero_()
-        self.cuda_graph_start_loc[1:bs] = torch.cumsum(seq_lens[: bs - 1], dim=0)
-
-    def get_cuda_graph_seq_len_fill_value(self):
-        return 1
-
     def forward_extend(
         self,
         q,