Remove VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE (#5272)

`VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE` is only used together with
`VLLM_ASCEND_ENABLE_PREFETCH_MLP` which is useless totally. This PR
remove it.
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

docs/source/tutorials/Qwen3-Dense.md

@@ -165,8 +165,8 @@ export TASK_QUEUE_ENABLE=1
 # Enable the AIVector core to directly schedule ROCE communication
 export HCCL_OP_EXPANSION_MODE="AIV"
 
-# Enable dense model and general optimizations for better performance.
-export VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE=1
+# Enable MLP prefetch for better performance.
+export VLLM_ASCEND_ENABLE_PREFETCH_MLP=1
 
 # Enable FlashComm_v1 optimization when tensor parallel is enabled.
 export VLLM_ASCEND_ENABLE_FLASHCOMM1=1
@@ -334,7 +334,7 @@ In dense model scenarios, the MLP's gate_up_proj and down_proj linear layers oft
 
 It is important to emphasize that, since we use vector computations to hide the weight prefetching pipeline, the setting of the prefetch buffer size is crucial. If the buffer size is too small, the optimization benefits will not be fully realized, while a larger buffer size may lead to resource contention, resulting in performance degradation. To accommodate different scenarios, we have exposed two environment variables `VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE` and `VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE` to allow for flexible buffer size configuration based on the specific workload.
 
-This optimization requires setting the environment variable `VLLM_ASCEND_ENABLE_PREFETCH_MLP = 1` and `VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE = 1` to be enabled.
+This optimization requires setting the environment variable `VLLM_ASCEND_ENABLE_PREFETCH_MLP = 1` to be enabled.
 
 ### 6. Zerolike Elimination
 This elimination removes unnecessary operations related to zero-like tensors in Attention forward, improving the efficiency of matrix operations and reducing memory usage.

tests/e2e/multicard/test_offline_inference_distributed.py

@@ -178,7 +178,6 @@ def test_deepseek_v2_lite_fc1_tp2() -> None:
 
 
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
-@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
 def test_qwen3_dense_fc1_tp2(model):
     example_prompts = [
@@ -197,7 +196,6 @@ def test_qwen3_dense_fc1_tp2(model):
 
 
 @pytest.mark.parametrize("model", QWEN_DENSE_MODELS)
-@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1"})
 @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"})
 def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
     example_prompts = [

tests/e2e/nightly/features/test_qwen3_32b_int8_a3_feature_stack3.py

@@ -72,7 +72,6 @@ async def test_models(model: str, tp_size: int) -> None:
         "OMP_PROC_BIND": "false",
         "VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1",
         "VLLM_ASCEND_ENABLE_FLASHCOMM": "1",
-        "VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1",
         "VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"
     }
     server_args = [

tests/e2e/nightly/models/test_qwen3_32b_int8.py

@@ -81,7 +81,6 @@ async def test_models(model: str, mode: str, tp_size: int) -> None:
     port = get_open_port()
     env_dict = {
         "TASK_QUEUE_ENABLE": "1",
-        "VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE": "1",
         "HCCL_OP_EXPANSION_MODE": "AIV",
         "VLLM_ASCEND_ENABLE_FLASHCOMM": "1",
         "VLLM_ASCEND_ENABLE_PREFETCH_MLP": "1"

vllm_ascend/ascend_forward_context.py

@@ -102,8 +102,7 @@ def set_ascend_forward_context(
 
         # TODO(rjg-lyh): refactor mlp weight prefetch method
         # set for mlp weight prefetch
-        prefetch_mlp_enabled = envs_ascend.VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE and \
-            envs_ascend.VLLM_ASCEND_ENABLE_PREFETCH_MLP and \
+        prefetch_mlp_enabled = envs_ascend.VLLM_ASCEND_ENABLE_PREFETCH_MLP and \
             forward_context.layer_idx is not None and \
             num_tokens is not None and num_tokens < 500
         if prefetch_mlp_enabled:

vllm_ascend/envs.py

@@ -108,11 +108,6 @@ env_variables: Dict[str, Callable[[], Any]] = {
     "VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE":
     lambda: int(
         os.getenv("VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE", 18 * 1024 * 1024)),
-    # Whether to enable dense model and general optimizations for better performance.
-    # Since we modified the base parent class `linear`, this optimization is also applicable to other model types.
-    # However, there might be hidden issues, and it is currently recommended to prioritize its use with dense models.
-    "VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE":
-    lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE", '0'))),
     # Whether to enable msMonitor tool to monitor the performance of vllm-ascend.
     "MSMONITOR_USE_DAEMON":
     lambda: bool(int(os.getenv("MSMONITOR_USE_DAEMON", '0'))),

vllm_ascend/ops/linear_op.py

@@ -53,13 +53,13 @@ from vllm.distributed import (split_tensor_along_last_dim,
 from vllm.distributed.parallel_state import get_tp_group
 from vllm.forward_context import get_forward_context
 
+from vllm_ascend import envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.parallel_state import (get_flashcomm2_odp_group,
                                                     get_flashcomm2_otp_group,
                                                     get_mlp_tp_group,
                                                     get_otp_group)
-from vllm_ascend.utils import (dense_optim_enable, enable_sp,
-                               flashcomm2_enable,
+from vllm_ascend.utils import (enable_sp, flashcomm2_enable,
                                get_flashcomm2_reorgnized_batch_ids,
                                matmul_allreduce_enable, mlp_tp_enable,
                                oproj_tp_enable, shared_expert_dp_enabled)
@@ -135,7 +135,7 @@ class CustomRowParallelOp(CustomLinearOp):
 
     def apply(self, input_):
         output, output_bias = self.apply_impl(input_)
-        if dense_optim_enable():
+        if envs_ascend.VLLM_ASCEND_ENABLE_PREFETCH_MLP:
             torch.ops.vllm.maybe_prefetch_mlp_gate_up_proj(output, self.prefix)
         if not self.return_bias:
             return output

vllm_ascend/utils.py

@@ -772,10 +772,6 @@ def matmul_allreduce_enable() -> bool:
     return envs_ascend.VLLM_ASCEND_ENABLE_MATMUL_ALLREDUCE
 
 
-def dense_optim_enable() -> bool:
-    return envs_ascend.VLLM_ASCEND_ENABLE_DENSE_OPTIMIZE
-
-
 def enable_sp(vllm_config=None, enable_shared_expert_dp: bool = False) -> bool:
     global _ENABLE_SP
     if _ENABLE_SP is None: