feat: support compatibility between MTP and two-batch-overlap (#7225)

Co-authored-by: Cheng Wan <54331508+ch-wan@users.noreply.github.com>

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

@@ -119,21 +119,27 @@ class TboAttnBackend(AttentionBackend):
         replay_seq_lens_sum: int = None,
         replay_seq_lens_cpu: Optional[torch.Tensor] = None,
     ):
+        token_num_per_seq = two_batch_overlap.get_token_num_per_seq(
+            forward_mode=forward_mode, spec_info=spec_info
+        )
         if fn_name == "init_forward_metadata_capture_cuda_graph":
-            assert capture_num_tokens == bs, "Only support num_tokens==bs currently"
+            assert (
-        num_tokens = bs
+                capture_num_tokens == bs * token_num_per_seq
+            ), "For target-verify or decode mode, num_tokens should be equal to token_num_per_seq * bs"
+        num_tokens = bs * token_num_per_seq
 
         tbo_split_seq_index, tbo_split_token_index = (
             two_batch_overlap.compute_split_indices_for_cuda_graph_replay(
                 forward_mode=forward_mode,
                 cuda_graph_num_tokens=num_tokens,
+                spec_info=spec_info,
             )
         )
 
         num_tokens_child_left = tbo_split_token_index
         num_tokens_child_right = num_tokens - tbo_split_token_index
-        bs_child_left = num_tokens_child_left
+        bs_child_left = tbo_split_seq_index
-        bs_child_right = num_tokens_child_right
+        bs_child_right = bs - bs_child_left
 
         assert (
             num_tokens_child_left > 0 and num_tokens_child_right > 0
@@ -190,16 +196,36 @@ def _init_forward_metadata_cuda_graph_split(
     seq_lens: torch.Tensor,
     encoder_lens: Optional[torch.Tensor],
     forward_mode: "ForwardMode",
-    spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
+    spec_info: Optional[EagleVerifyInput],
     # capture args
     capture_num_tokens: int = None,
     # replay args
     replay_seq_lens_sum: int = None,
     replay_seq_lens_cpu: Optional[torch.Tensor] = None,
 ):
+    token_num_per_seq = two_batch_overlap.get_token_num_per_seq(
+        forward_mode=forward_mode, spec_info=spec_info
+    )
     assert encoder_lens is None, "encoder_lens is not supported yet"
-    assert spec_info is None, "spec_info is not supported yet"
+    if spec_info is not None:
+        output_spec_info = two_batch_overlap.split_spec_info(
+            spec_info=spec_info,
+            start_seq_index=seq_slice.start if seq_slice.start is not None else 0,
+            end_seq_index=seq_slice.stop if seq_slice.stop is not None else bs,
+            start_token_index=(
+                seq_slice.start * token_num_per_seq
+                if seq_slice.start is not None
+                else 0
+            ),
+            end_token_index=(
+                seq_slice.stop * token_num_per_seq
+                if seq_slice.stop is not None
+                else bs * token_num_per_seq
+            ),
+        )
 
+    else:
+        output_spec_info = None
     ans = dict(
         bs=output_bs,
         req_pool_indices=req_pool_indices[seq_slice],
@@ -208,14 +234,16 @@ def _init_forward_metadata_cuda_graph_split(
         forward_mode=forward_mode,
         # ignore
         encoder_lens=None,
-        spec_info=None,
+        spec_info=output_spec_info,
     )
 
     if fn_name == "init_forward_metadata_capture_cuda_graph":
-        assert capture_num_tokens == bs, "Only support num_tokens==bs currently"
+        assert (
+            capture_num_tokens == bs * token_num_per_seq
+        ), "Only support num_tokens==bs * token_num_per_seq for target-verify or decode mode"
         ans.update(
             dict(
-                num_tokens=output_bs,
+                num_tokens=output_bs * token_num_per_seq,
             )
         )
     elif fn_name == "init_forward_metadata_replay_cuda_graph":

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -679,6 +679,7 @@ class CudaGraphRunner:
                 forward_mode=self.capture_forward_mode,
                 bs=bs,
                 num_token_non_padded=len(forward_batch.input_ids),
+                spec_info=forward_batch.spec_info,
             )
         if forward_batch.forward_mode.is_idle() and forward_batch.spec_info is not None:
             forward_batch.spec_info.custom_mask = self.custom_mask

python/sglang/srt/model_executor/forward_batch_info.py

@@ -352,7 +352,9 @@ class ForwardBatch:
 
         if ret.forward_mode.is_idle():
             ret.positions = torch.empty((0,), device=device)
-            TboForwardBatchPreparer.prepare(ret)
+            TboForwardBatchPreparer.prepare(
+                ret, is_draft_worker=model_runner.is_draft_worker
+            )
             return ret
 
         # Override the positions with spec_info
@@ -397,7 +399,9 @@ class ForwardBatch:
         if model_runner.server_args.lora_paths is not None:
             model_runner.lora_manager.prepare_lora_batch(ret)
 
-        TboForwardBatchPreparer.prepare(ret)
+        TboForwardBatchPreparer.prepare(
+            ret, is_draft_worker=model_runner.is_draft_worker
+        )
 
         return ret
 

python/sglang/srt/model_executor/model_runner.py

@@ -1039,7 +1039,7 @@ class ModelRunner:
 
     def init_attention_backend(self):
         """Init attention kernel backend."""
-        if self.server_args.enable_two_batch_overlap:
+        if self.server_args.enable_two_batch_overlap and not self.is_draft_worker:
             self.attn_backend = TboAttnBackend.init_new(self._get_attention_backend)
         else:
             self.attn_backend = self._get_attention_backend()

python/sglang/srt/operations_strategy.py

@@ -71,7 +71,9 @@ def _compute_moe_deepseek_layer_operations_strategy_tbo(
     assert layer.is_layer_sparse, "dense layer TBO not yet implemented"
     if forward_mode == ForwardMode.EXTEND:
         return _compute_moe_deepseek_blog_prefill(layer)
-    elif forward_mode == ForwardMode.DECODE:
+    elif (
+        forward_mode == ForwardMode.DECODE or forward_mode == ForwardMode.TARGET_VERIFY
+    ):
         return _compute_moe_deepseek_blog_decode(layer)
     else:
         raise NotImplementedError(f"Unsupported {forward_mode=}")
@@ -146,7 +148,9 @@ def _compute_moe_qwen3_layer_operations_strategy_tbo(
     assert layer.is_layer_sparse, "qwen3 moe only support sparse layers"
     if forward_mode == ForwardMode.EXTEND:
         return _compute_moe_qwen3_prefill(layer)
-    elif forward_mode == ForwardMode.DECODE:
+    elif (
+        forward_mode == ForwardMode.DECODE or forward_mode == ForwardMode.TARGET_VERIFY
+    ):
         return _compute_moe_qwen3_decode(layer)
     else:
         raise NotImplementedError(f"Unsupported {forward_mode=}")

python/sglang/srt/two_batch_overlap.py

@@ -1,6 +1,7 @@
 import dataclasses
 import logging
-from typing import Dict, List, Optional, Sequence
+from dataclasses import replace
+from typing import Dict, List, Optional, Sequence, Union
 
 import torch
 
@@ -16,6 +17,7 @@ from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 from sglang.srt.operations import execute_operations, execute_overlapped_operations
 from sglang.srt.operations_strategy import OperationsStrategy
+from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
 from sglang.srt.utils import BumpAllocator, DeepEPMode, get_bool_env_var
 
 _tbo_debug = get_bool_env_var("SGLANG_TBO_DEBUG")
@@ -26,17 +28,34 @@ logger = logging.getLogger(__name__)
 # -------------------------------- Compute Basic Info ---------------------------------------
 
 
+def get_token_num_per_seq(
+    forward_mode: ForwardMode,
+    spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None,
+):
+    if forward_mode.is_target_verify():
+        return spec_info.draft_token_num
+    elif forward_mode.is_decode():
+        return 1
+    elif forward_mode.is_idle():
+        return 0
+    else:
+        # For extend, we should not use `token_num_per_seq`.
+        return None
+
+
 # TODO: may smartly disable TBO when batch size is too small b/c it will slow down
 def compute_split_seq_index(
     forward_mode: "ForwardMode",
     num_tokens: int,
     extend_lens: Optional[Sequence[int]],
+    token_num_per_seq: Optional[int],
 ) -> Optional[int]:
-    if forward_mode.is_extend():
+    if forward_mode == ForwardMode.EXTEND:
         assert extend_lens is not None
         return _split_array_by_half_sum(extend_lens)
-    elif forward_mode.is_decode():
+    elif forward_mode.is_target_verify() or forward_mode.is_decode():
-        return num_tokens // 2
+        assert token_num_per_seq is not None
+        return (num_tokens // token_num_per_seq) // 2
     elif forward_mode.is_idle():
         assert num_tokens == 0
         return 0
@@ -63,16 +82,103 @@ def _split_array_by_half_sum(arr: Sequence[int]) -> int:
     return best_index
 
 
+def _compute_mask_offset(seq_index: int, spec_info: Optional[EagleVerifyInput]) -> int:
+    if seq_index == 0:
+        return 0
+
+    offset = 0
+    max_seq_len = min(seq_index, spec_info.seq_lens_cpu.shape[0])
+    for i in range(max_seq_len):
+        offset += (
+            spec_info.seq_lens_cpu[i] + spec_info.draft_token_num
+        ) * spec_info.draft_token_num
+    return offset
+
+
+def split_spec_info(
+    spec_info: Optional[EagleVerifyInput],
+    start_seq_index: int,
+    end_seq_index: int,
+    start_token_index: int,
+    end_token_index: int,
+):
+    if spec_info is None:
+        return None
+    if spec_info.draft_token is not None:
+        draft_token = spec_info.draft_token[start_token_index:end_token_index]
+    else:
+        draft_token = None
+    if spec_info.custom_mask is not None and spec_info.draft_token is not None:
+        custom_mask_start = _compute_mask_offset(start_seq_index, spec_info)
+        if end_seq_index == spec_info.seq_lens_cpu.shape[0]:
+            custom_mask_end = spec_info.custom_mask.shape[0]
+        else:
+            custom_mask_end = _compute_mask_offset(end_seq_index, spec_info)
+
+        if custom_mask_end > custom_mask_start:
+            custom_mask = spec_info.custom_mask[custom_mask_start:custom_mask_end]
+        else:
+            custom_mask = spec_info.custom_mask
+    else:
+        custom_mask = spec_info.custom_mask
+    if spec_info.positions is not None:
+        positions = spec_info.positions[start_token_index:end_token_index]
+    else:
+        positions = None
+    if spec_info.retrive_index is not None:
+        retrive_index = spec_info.retrive_index[start_seq_index:end_seq_index]
+    else:
+        retrive_index = None
+    if spec_info.retrive_next_token is not None:
+        retrive_next_token = spec_info.retrive_next_token[start_seq_index:end_seq_index]
+    else:
+        retrive_next_token = None
+    if spec_info.retrive_next_sibling is not None:
+        retrive_next_sibling = spec_info.retrive_next_sibling[
+            start_seq_index:end_seq_index
+        ]
+    else:
+        retrive_next_sibling = None
+    if spec_info.retrive_cum_len is not None:
+        retrive_cum_len = spec_info.retrive_cum_len[start_seq_index:end_seq_index]
+    else:
+        retrive_cum_len = None
+
+    if spec_info.seq_lens_cpu is not None:
+        seq_lens_cpu = spec_info.seq_lens_cpu[start_seq_index:end_seq_index]
+    else:
+        seq_lens_cpu = None
+    if seq_lens_cpu is not None:
+        seq_lens_sum = seq_lens_cpu.sum()
+    else:
+        seq_lens_sum = None
+    output_spec_info = replace(
+        spec_info,
+        custom_mask=custom_mask,
+        draft_token=draft_token,
+        positions=positions,
+        retrive_index=retrive_index,
+        retrive_next_token=retrive_next_token,
+        retrive_next_sibling=retrive_next_sibling,
+        retrive_cum_len=retrive_cum_len,
+        seq_lens_cpu=seq_lens_cpu,
+        seq_lens_sum=seq_lens_sum,
+    )
+    return output_spec_info
+
+
 def compute_split_token_index(
     split_seq_index: int,
     forward_mode: "ForwardMode",
     extend_seq_lens: Optional[Sequence[int]],
+    token_num_per_seq: Optional[int],
 ) -> int:
-    if forward_mode.is_extend():
+    if forward_mode == ForwardMode.EXTEND:
         assert extend_seq_lens is not None
         return sum(extend_seq_lens[:split_seq_index])
-    elif forward_mode.is_decode():
+    elif forward_mode.is_target_verify() or forward_mode.is_decode():
-        return split_seq_index
+        assert token_num_per_seq is not None
+        return split_seq_index * token_num_per_seq
     elif forward_mode.is_idle():
         assert split_seq_index == 0
         return 0
@@ -83,19 +189,25 @@ def compute_split_token_index(
 def compute_split_indices_for_cuda_graph_replay(
     forward_mode: ForwardMode,
     cuda_graph_num_tokens: int,
+    spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
 ):
     forward_mode_for_tbo_split = (
         forward_mode if forward_mode != ForwardMode.IDLE else ForwardMode.DECODE
     )
+    token_num_per_seq = get_token_num_per_seq(
+        forward_mode=forward_mode, spec_info=spec_info
+    )
     tbo_split_seq_index = compute_split_seq_index(
         forward_mode=forward_mode_for_tbo_split,
         num_tokens=cuda_graph_num_tokens,
         extend_lens=None,
+        token_num_per_seq=token_num_per_seq,
     )
     tbo_split_token_index = compute_split_token_index(
         split_seq_index=tbo_split_seq_index,
         forward_mode=forward_mode_for_tbo_split,
         extend_seq_lens=None,
+        token_num_per_seq=token_num_per_seq,
     )
     return tbo_split_seq_index, tbo_split_token_index
 
@@ -110,11 +222,15 @@ class TboCudaGraphRunnerPlugin:
     def capture_one_batch_size(self, batch: ForwardBatch, num_tokens: int):
         if not global_server_args_dict["enable_two_batch_overlap"]:
             return
+        token_num_per_seq = get_token_num_per_seq(
+            forward_mode=batch.forward_mode, spec_info=batch.spec_info
+        )
 
         batch.tbo_split_seq_index = compute_split_seq_index(
             forward_mode=batch.forward_mode,
             num_tokens=num_tokens,
             extend_lens=None,
+            token_num_per_seq=token_num_per_seq,
         )
         # For simplicity, when two_batch_overlap is enabled, we only capture CUDA Graph for tbo=true
         assert batch.tbo_split_seq_index is not None, f"{num_tokens=}"
@@ -129,13 +245,20 @@ class TboCudaGraphRunnerPlugin:
         )
 
     def replay_prepare(
-        self, forward_mode: ForwardMode, bs: int, num_token_non_padded: int
+        self,
+        forward_mode: ForwardMode,
+        bs: int,
+        num_token_non_padded: int,
+        spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]],
     ):
+        token_num_per_seq = get_token_num_per_seq(
+            forward_mode=forward_mode, spec_info=spec_info
+        )
         tbo_split_seq_index, tbo_split_token_index = (
             compute_split_indices_for_cuda_graph_replay(
                 forward_mode=forward_mode,
-                # TODO support bs!=num_tokens
+                cuda_graph_num_tokens=bs * token_num_per_seq,
-                cuda_graph_num_tokens=bs,
+                spec_info=spec_info,
             )
         )
 
@@ -154,14 +277,29 @@ class TboDPAttentionPreparer:
         self.enable_two_batch_overlap = enable_two_batch_overlap
 
         if local_batch is not None:
+            token_num_per_seq = get_token_num_per_seq(
+                forward_mode=local_batch.forward_mode, spec_info=local_batch.spec_info
+            )
+
+            if (
+                local_batch.forward_mode.is_target_verify()
+                or local_batch.forward_mode.is_decode()
+            ):
+                num_tokens = local_batch.batch_size() * token_num_per_seq
+            else:
+                num_tokens = local_batch.extend_num_tokens
             self.local_tbo_split_seq_index = compute_split_seq_index(
                 forward_mode=local_batch.forward_mode,
-                num_tokens=local_batch.input_ids.shape[0],
+                num_tokens=num_tokens,
                 extend_lens=local_batch.extend_lens,
+                token_num_per_seq=token_num_per_seq,
             )
             resolved_deepep_mode = deepep_mode.resolve(local_batch.forward_mode)
             local_can_run_tbo = (self.local_tbo_split_seq_index is not None) and not (
-                local_batch.forward_mode.is_extend()
+                (
+                    local_batch.forward_mode.is_extend()
+                    and not local_batch.forward_mode.is_target_verify()
+                )
                 and enable_deepep_moe
                 and (resolved_deepep_mode == DeepEPMode.low_latency)
             )
@@ -218,8 +356,8 @@ class TboDPAttentionPreparer:
 
 class TboForwardBatchPreparer:
     @classmethod
-    def prepare(cls, batch: ForwardBatch):
+    def prepare(cls, batch: ForwardBatch, is_draft_worker: bool = False):
-        if batch.tbo_split_seq_index is None:
+        if batch.tbo_split_seq_index is None or is_draft_worker:
             return
 
         tbo_children_num_token_non_padded = (
@@ -242,7 +380,9 @@ class TboForwardBatchPreparer:
                 f"TboForwardBatchPreparer.prepare "
                 f"tbo_split_seq_index={batch.tbo_split_seq_index} "
                 f"tbo_split_token_index={tbo_split_token_index} "
-                f"extend_seq_lens={batch.extend_seq_lens_cpu}"
+                f"extend_seq_lens={batch.extend_seq_lens_cpu} "
+                f"bs={batch.batch_size} "
+                f"forward_mode={batch.forward_mode}"
             )
 
         assert isinstance(batch.attn_backend, TboAttnBackend)
@@ -286,6 +426,9 @@ class TboForwardBatchPreparer:
         output_attn_backend: AttentionBackend,
         out_num_token_non_padded: torch.Tensor,
     ):
+        assert (
+            end_token_index >= start_token_index
+        ), f"{end_token_index=}, {start_token_index=}, batch={batch}"
         num_tokens = batch.input_ids.shape[0]
         num_seqs = batch.batch_size
 
@@ -317,11 +460,30 @@ class TboForwardBatchPreparer:
             old_value = getattr(batch, key)
             if old_value is None:
                 continue
+            elif batch.forward_mode.is_target_verify() and (
+                key == "extend_seq_lens"
+                or key == "extend_prefix_lens"
+                or key == "extend_start_loc"
+                or key == "extend_prefix_lens_cpu"
+                or key == "extend_seq_lens_cpu"
+                or key == "extend_logprob_start_lens_cpu"
+            ):
+                output_dict[key] = None
+                continue
             assert (
                 len(old_value) == num_seqs
             ), f"{key=} {old_value=} {num_seqs=} {batch=}"
             output_dict[key] = old_value[start_seq_index:end_seq_index]
 
+        spec_info = getattr(batch, "spec_info")
+        output_spec_info = split_spec_info(
+            spec_info=spec_info,
+            start_token_index=start_token_index,
+            end_token_index=end_token_index,
+            start_seq_index=start_seq_index,
+            end_seq_index=end_seq_index,
+        )
+        output_dict["spec_info"] = output_spec_info
         for key in [
             "forward_mode",
             "return_logprob",
@@ -329,18 +491,17 @@ class TboForwardBatchPreparer:
             "token_to_kv_pool",
             "can_run_dp_cuda_graph",
             "global_forward_mode",
-            "spec_info",
             "spec_algorithm",
             "capture_hidden_mode",
             "padded_static_len",
             "mrope_positions",  # only used by qwen2-vl, thus not care
         ]:
             output_dict[key] = getattr(batch, key)
-
+        if not batch.forward_mode.is_target_verify():
-        assert (
+            assert (
-            _compute_extend_num_tokens(batch.input_ids, batch.forward_mode)
+                _compute_extend_num_tokens(batch.input_ids, batch.forward_mode)
-            == batch.extend_num_tokens
+                == batch.extend_num_tokens
-        ), f"{batch=}"
+            ), f"{batch=}"
         extend_num_tokens = _compute_extend_num_tokens(
             output_dict["input_ids"], output_dict["forward_mode"]
         )
@@ -419,18 +580,26 @@ class TboForwardBatchPreparer:
 
     @classmethod
     def _compute_split_token_index(cls, batch: ForwardBatch):
+        token_num_per_seq = get_token_num_per_seq(
+            forward_mode=batch.forward_mode, spec_info=batch.spec_info
+        )
         return compute_split_token_index(
             split_seq_index=batch.tbo_split_seq_index,
             forward_mode=batch.forward_mode,
             extend_seq_lens=batch.extend_seq_lens_cpu,
+            token_num_per_seq=token_num_per_seq,
         )
 
 
 def _compute_extend_num_tokens(input_ids, forward_mode: ForwardMode):
-    if forward_mode.is_extend():
+    if (
-        return input_ids.shape[0]
+        forward_mode.is_decode()
-    elif forward_mode.is_decode() or forward_mode.is_idle():
+        or forward_mode.is_idle()
+        or forward_mode.is_target_verify()
+    ):
         return None
+    elif forward_mode.is_extend():
+        return input_ids.shape[0]
     raise NotImplementedError
 
 

test/srt/test_dp_attention.py

@@ -137,5 +137,86 @@ class TestDPAttentionDP2TP2DeepseekV3MTP(CustomTestCase):
         self.assertGreater(avg_spec_accept_length, 2.5)
 
 
+# TODO: enable this test later
+# class TestDPAttentionDP2TP2DeepseekV3MTPTBO(CustomTestCase):
+#     @classmethod
+#     def setUpClass(cls):
+#         import os
+
+#         # print debug log for tbo
+#         os.environ["SGLANG_TBO_DEBUG"] = "1"
+#         cls.model = DEFAULT_MODEL_NAME_FOR_TEST_MLA
+#         cls.base_url = DEFAULT_URL_FOR_TEST
+#         other_args = [
+#             "--trust-remote-code",
+#             "--disable-radix",
+#             "--speculative-algorithm",
+#             "EAGLE",
+#             "--speculative-num-steps",
+#             "2",
+#             "--speculative-eagle-topk",
+#             "4",
+#             "--speculative-num-draft-tokens",
+#             "4",
+#             "--speculative-draft",
+#             DEFAULT_MODEL_NAME_FOR_TEST_MLA_NEXTN,
+#             "--tp-size",
+#             "2",
+#             "--enable-dp-attention",
+#             "--dp-size",
+#             "2",
+#             "--enable-two-batch-overlap",
+#             "--enable-deepep-moe",
+#             "--deepep-mode",
+#             "low_latency",
+#             "--chunked-prefill-size",
+#             "256",
+#             "--cuda-graph-max-bs",
+#             "32",
+#             "--max-running-requests",
+#             "32",
+#         ]
+#         if not is_in_amd_ci():
+#             other_args += ["--mem-frac", "0.7"]
+#         cls.process = popen_launch_server(
+#             cls.model,
+#             cls.base_url,
+#             timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+#             other_args=other_args,
+#         )
+
+#     @classmethod
+#     def tearDownClass(cls):
+#         kill_process_tree(cls.process.pid)
+
+#     def test_gsm8k(self):
+#         requests.get(self.base_url + "/flush_cache")
+
+#         args = SimpleNamespace(
+#             num_shots=5,
+#             data_path=None,
+#             num_questions=200,
+#             max_new_tokens=512,
+#             parallel=128,
+#             host="http://127.0.0.1",
+#             port=int(self.base_url.split(":")[-1]),
+#         )
+#         metrics = run_eval_few_shot_gsm8k(args)
+#         print(metrics)
+
+#         self.assertGreater(metrics["accuracy"], 0.60)
+
+#         server_info = requests.get(self.base_url + "/get_server_info")
+#         avg_spec_accept_length = server_info.json()["internal_states"][0][
+#             "avg_spec_accept_length"
+#         ]
+#         print(
+#             f"###test_gsm8k (deepseek-v3 mtp + dp + tbo):\n"
+#             f"accuracy={metrics['accuracy']=:.3f}\n"
+#             f"{avg_spec_accept_length=:.3f}\n"
+#         )
+#         self.assertGreater(avg_spec_accept_length, 2.3)
+
+
 if __name__ == "__main__":
     unittest.main()