[bugfix] some bugs maybe fail to run (#896)

### What this PR does / why we need it?
Solve the bug that the graph mode is the same as p and d, and some other
bugs.
### Does this PR introduce _any_ user-facing change?
Wouldn't be
### How was this patch tested?
Follow the end-to-end test

Signed-off-by: ningbenzhe1 <ningbenzhe@huawei.com>

Author: NINGBENZHE

tests/long_term/spec_decode/e2e/test_v1_mtp_correctness.py

@@ -89,4 +89,4 @@ def test_mtp_correctness(
         # Heuristic: expect at least 66% of the prompts to match exactly
         # Upon failure, inspect the outputs to check for inaccuracy.
         assert matches > int(0.66 * len(ref_outputs))
-        del spec_llm
+        del spec_llm

vllm_ascend/attention/attention_v1.py

@@ -129,6 +129,9 @@ class AscendMetadata:
     attn_state: AscendAttentionState = AscendAttentionState.ChunkedPrefill
     attn_mask: Optional[torch.Tensor] = None
 
+    # For logging.
+    num_input_tokens: int = 0  # Number of tokens including padding.
+
 
 class AscendAttentionMetadataBuilder:
 

vllm_ascend/distributed/parallel_state.py

@@ -21,12 +21,18 @@ def get_etp_group() -> GroupCoordinator:
     return _ETP
 
 
+def model_parallel_initialized():
+    return (_ETP is not None and _EP is not None)
+
+
 def init_ascend_model_parallel(
     expert_parallel_size: int = 1,
     expert_tensor_parallel_size: int = 1,
     world_size: Optional[int] = None,
     backend: Optional[str] = None,
 ):
+    if model_parallel_initialized():
+        return
     assert torch.distributed.is_initialized()
     world_size = world_size or torch.distributed.get_world_size()
     backend = backend or torch.distributed.get_backend(

vllm_ascend/ops/fused_moe.py

@@ -66,8 +66,7 @@ def fused_experts_with_mc2(
     local_rank = torch.distributed.get_rank(group=ep_group)
     all_to_all_group_size = torch.distributed.get_world_size(ep_group)
 
-    world_szie = torch.distributed.get_world_size()
-    tp_size = world_szie // all_to_all_group_size
+    tp_size = get_etp_group().world_size
     tp_rank = rank % tp_size
 
     stage1_kwargs = {

vllm_ascend/patch/platform/patch_common/patch_distributed.py

@@ -20,6 +20,7 @@
 import torch
 import vllm
 import vllm.distributed
+import vllm.envs as envs
 from torch.distributed import ProcessGroup
 from torch.distributed.distributed_c10d import (Backend, PrefixStore,
                                                 _get_default_timeout,
@@ -164,10 +165,9 @@ def parallel_config_get_dp_port(self) -> int:
     """
     answer = self.data_parallel_master_port
     self.data_parallel_master_port += 1
-    import os
 
     # NOTE: Get port from envs directly when using torchrun
-    port = int(os.environ.get("MASTER_PORT", answer))  # type: ignore
+    port = envs.VLLM_DP_MASTER_PORT if envs.VLLM_DP_MASTER_PORT else answer
     return port
 
 

vllm_ascend/worker/mtp_proposer_v1.py

@@ -219,4 +219,4 @@ def prepare_input_kernel(out_ptr: torch.Tensor, cu_query_lens: torch.Tensor,
 
     global_indices_flat = global_indices[mask]
     values_flat = values[mask]
-    out_ptr[global_indices_flat] = values_flat
+    out_ptr[global_indices_flat] = values_flat

vllm_ascend/worker/worker_v1.py

@@ -173,7 +173,7 @@ def execute_model(
         scheduler_output: "SchedulerOutput",
     ) -> Optional[ModelRunnerOutput]:
         output = self.model_runner.execute_model(scheduler_output)
-        return output if self.rank == 0 else None
+        return output if self.is_driver_worker else None
 
     def load_model(self) -> None:
         self.model_runner.load_model()