diff --git a/vllm_ascend/ascend_forward_context.py b/vllm_ascend/ascend_forward_context.py
index e138465366f..8d51a9bd9a4 100644
--- a/vllm_ascend/ascend_forward_context.py
+++ b/vllm_ascend/ascend_forward_context.py
@@ -12,7 +12,7 @@
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.utils import (
     AscendDeviceType,
-    enable_flash_comm_v1,
+    enable_sp,
     flashcomm2_enable,
     get_ascend_device_type,
     has_layer_idx,
@@ -91,14 +91,14 @@ def set_ascend_forward_context(
         # main model and drafter model may have different architecture
         is_context_moe_model = is_drafter_moe_model(vllm_config) if is_draft_model else is_moe_model(vllm_config)
         if is_context_moe_model:
-            flash_comm_v1_enabled = enable_flash_comm_v1() and num_tokens is not None
+            flash_comm_v1_enabled = enable_sp(vllm_config) and num_tokens is not None
             mmrs_fusion = False
         elif is_draft_model:
             # TODO: for dense drafter, `sp` is redundant and is not compatible with `dp` and `graph`.
             # Disable it to avoid more problems.
             flash_comm_v1_enabled = False
         else:
-            flash_comm_v1_enabled = enable_flash_comm_v1() and num_tokens is not None and num_tokens > 1000
+            flash_comm_v1_enabled = enable_sp(vllm_config) and num_tokens is not None and num_tokens > 1000
         forward_context.mmrs_fusion = mmrs_fusion
         forward_context.num_tokens = num_tokens
         forward_context.flash_comm_v1_enabled = flash_comm_v1_enabled
diff --git a/vllm_ascend/ops/linear.py b/vllm_ascend/ops/linear.py
index 53bd6e06161..b21186060be 100644
--- a/vllm_ascend/ops/linear.py
+++ b/vllm_ascend/ops/linear.py
@@ -40,7 +40,7 @@
 from vllm.model_executor.utils import set_weight_attrs
 
 from vllm_ascend.ops.linear_op import get_parallel_op, get_replicated_op
-from vllm_ascend.utils import enable_flash_comm_v1, maybe_trans_nz
+from vllm_ascend.utils import enable_sp, maybe_trans_nz
 
 
 class AscendUnquantizedLinearMethod(UnquantizedLinearMethod):
@@ -240,7 +240,7 @@ def __init__(
         disable_tp: bool = False,
     ):
         # TODO(kunpengW-code): Specifying the prefix in linear layers of some models in the vLLM.
-        if enable_flash_comm_v1():
+        if enable_sp():
             compilation_config = get_current_vllm_config().compilation_config
             unique_prefix = prefix
             if prefix in compilation_config.static_forward_context:
diff --git a/vllm_ascend/ops/linear_op.py b/vllm_ascend/ops/linear_op.py
index 9c78418f770..6706313bf89 100644
--- a/vllm_ascend/ops/linear_op.py
+++ b/vllm_ascend/ops/linear_op.py
@@ -70,7 +70,7 @@
 from vllm_ascend.utils import (
     enable_dsa_cp,
     enable_dsa_cp_with_layer_shard,
-    enable_flash_comm_v1,
+    enable_sp,
     flashcomm2_enable,
     get_flashcomm2_reorgnized_batch_ids,
     get_weight_prefetch_method,
@@ -466,7 +466,7 @@ def apply_impl(self, input_: torch.Tensor) -> torch.Tensor | tuple[torch.Tensor,
         # Matrix multiply.
         assert self.quant_method is not None
 
-        if enable_flash_comm_v1():
+        if enable_sp():
             input_ = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(input_, True)
 
         # Trigger async broadcast before matmul to overlap communication.
@@ -649,7 +649,7 @@ def _get_column_parallel_op(
     if flashcomm2_oshard_manager.flashcomm2_oshard_enable():
         if any(p in prefix for p in ("qkv_proj", "conv1d", "query_key_value")):
             return Flashcomm2OshardQKVParallelOp(layer)
-    if enable_flash_comm_v1():
+    if enable_sp():
         if "shared_expert" in prefix:
             return None
         sp_column_prefix = [
@@ -688,7 +688,7 @@ def _get_row_parallel_op(
     if flashcomm2_enable():
         if "o_proj" in prefix or "out_proj" in prefix:
             return Flashcomm2OProjRowParallelOp(layer)
-    if enable_flash_comm_v1():
+    if enable_sp():
         if "shared_expert" in prefix:
             return None
         sp_row_prefixes = [
diff --git a/vllm_ascend/platform.py b/vllm_ascend/platform.py
index d3c926b6f87..c0e5298426a 100644
--- a/vllm_ascend/platform.py
+++ b/vllm_ascend/platform.py
@@ -39,7 +39,6 @@
     COMPRESSED_TENSORS_METHOD,
     AscendDeviceType,
     check_kv_extra_config,
-    enable_sp,
     flashcomm2_enable,
     get_ascend_device_type,
     is_moe_model,
@@ -48,7 +47,7 @@
     update_aclgraph_sizes,
     update_cudagraph_capture_sizes,
     is_310p,
-    enable_flash_comm_v1,
+    enable_sp,
 )
 
 if TYPE_CHECKING:
@@ -402,7 +401,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
             )
             vllm_config.parallel_config.cp_kv_cache_interleave_size = cache_config.block_size
 
-        if enable_flash_comm_v1():
+        if enable_sp(vllm_config):
             assert not is_vl_model(vllm_config), """Flash Comm V1 is not supported for VL models. \
                 Please disable it by setting VLLM_ASCEND_ENABLE_FLASHCOMM1=0. \
                 For optimal performance with VL models, we recommend enabling Sequence Parallelism \
diff --git a/vllm_ascend/utils.py b/vllm_ascend/utils.py
index 792af587555..a1f7083ed35 100644
--- a/vllm_ascend/utils.py
+++ b/vllm_ascend/utils.py
@@ -719,15 +719,6 @@ def matmul_allreduce_enable() -> bool:
     return envs_ascend.VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE
 
 
-def enable_flash_comm_v1():
-    return (
-        envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM1
-        # Flash comm 1 should be enabled by env VLLM_ASCEND_ENABLE_FLASHCOMM1
-        # We retain the env VLLM_ASCEND_ENABLE_FLASHCOMM here for backward compatibility.
-        or bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM", "0")))
-    )
-
-
 def enable_sp_by_pass(vllm_config: VllmConfig):
     return not vllm_config.model_config.enforce_eager and vllm_config.compilation_config.pass_config.enable_sp
 
@@ -739,7 +730,12 @@ def enable_sp(vllm_config=None, enable_shared_expert_dp: bool = False) -> bool:
             from vllm.config import get_current_vllm_config
 
             vllm_config = get_current_vllm_config()
-        _ENABLE_SP = enable_sp_by_pass(vllm_config) or enable_flash_comm_v1()
+        _ENABLE_SP = (
+            envs_ascend.VLLM_ASCEND_ENABLE_FLASHCOMM1
+            # Flash comm 1 should be enabled by env VLLM_ASCEND_ENABLE_FLASHCOMM1
+            # We retain the env VLLM_ASCEND_ENABLE_FLASHCOMM here for backward compatibility.
+            or bool(int(os.getenv("VLLM_ASCEND_ENABLE_FLASHCOMM", "0")))
+        )
 
         if not _ENABLE_SP and enable_shared_expert_dp:
             _ENABLE_SP = True
@@ -1104,7 +1100,7 @@ def enable_dsa_cp() -> bool:
     is_ds_v32 = hasattr(vllm_config.model_config, "hf_text_config") and hasattr(
         vllm_config.model_config.hf_text_config, "index_topk"
     )
-    return bool(is_ds_v32 and enable_flash_comm_v1())
+    return bool(is_ds_v32 and enable_sp())
 
 
 @lru_cache(maxsize=1)
diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index ee5dd81b1ef..a9c05cbc7e9 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -113,8 +113,8 @@
 from vllm_ascend.spec_decode.mtp_proposer import MtpProposer
 from vllm_ascend.utils import (
     check_gdn_layer,
-    enable_flash_comm_v1,
     enable_sp,
+    enable_sp_by_pass,
     is_drafter_moe_model,
     is_moe_model,
     lmhead_tp_enable,
@@ -1722,7 +1722,7 @@ def _pad_for_sequence_parallelism(self, num_scheduled_tokens: int) -> int:
         # Pad tokens to multiple of tensor_parallel_size when
         # enabled collective fusion for SP
         tp_size = self.vllm_config.parallel_config.tensor_parallel_size
-        if enable_sp(self.vllm_config):
+        if enable_sp(self.vllm_config) or enable_sp_by_pass(self.vllm_config):
             return round_up(num_scheduled_tokens, tp_size)
         return num_scheduled_tokens
 
@@ -2272,7 +2272,7 @@ def _dummy_run(
                 # tp_size; otherwise, on non-first PP ranks it would effectively perform an extra all-gather, leading
                 # to incorrect memory estimation and potentially causing OOM.
                 intermediate_tokens = num_tokens_padded
-                if enable_flash_comm_v1():
+                if enable_sp():
                     tp_size = get_tensor_model_parallel_world_size()
                     intermediate_tokens = (num_tokens_padded + tp_size - 1) // tp_size
                 if self.intermediate_tensors is None:
diff --git a/vllm_ascend/worker/worker.py b/vllm_ascend/worker/worker.py
index 1cadf05b233..5e65521f080 100644
--- a/vllm_ascend/worker/worker.py
+++ b/vllm_ascend/worker/worker.py
@@ -55,7 +55,7 @@
 from vllm_ascend.utils import (
     AscendDeviceType,
     check_ascend_device_type,
-    enable_flash_comm_v1,
+    enable_sp,
     get_ascend_device_type,
     register_ascend_customop,
 )
@@ -376,7 +376,7 @@ def execute_model(
         if forward_pass and not get_pp_group().is_first_rank:
             # If flashcomm1 is used, this all_gather_group parameter needs to be removed, otherwise
             # it will conflict with the all-gather operation in flashcomm1.
-            if enable_flash_comm_v1():
+            if enable_sp():
                 all_gather_group = None
             else:
                 all_gather_group = get_tp_group()
@@ -393,7 +393,7 @@ def execute_model(
         assert parallel_config.distributed_executor_backend != ("external_launcher") and not get_pp_group().is_last_rank
         # If flashcomm1 is used, this all_gather_group parameter needs to be removed, otherwise
         # it will conflict with the all-gather operation in flashcomm1.
-        if enable_flash_comm_v1():
+        if enable_sp():
             all_gather_group = None
         else:
             all_gather_group = get_tp_group()