[Dist][EP] Remove ETP/EP maintained in vllm-ascend (#1681)

### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced

This is a part of #1422 backport.

Fixes https://github.com/vllm-project/vllm-ascend/issues/1396
https://github.com/vllm-project/vllm-ascend/issues/1154

### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.

### How was this patch tested?
CI passed with new added and existing test.


- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a

Signed-off-by: MengqingCao <cmq0113@163.com>

vllm_ascend/platform.py

@@ -131,24 +131,6 @@ class NPUPlatform(Platform):
         if kv_cache_dtype is not None:
             vllm_config.cache_config.cache_dtype = kv_cache_dtype
 
-        if parallel_config:
-            # Default value for expert tensor parallel size
-            parallel_config.expert_tensor_parallel_size = parallel_config.tensor_parallel_size
-
-            # NOTE: When enable_expert_parallel is True, we follow vLLM convention:
-            # ep_size = world_size, which means expert_tensor_parallel_size must be 1
-            if parallel_config.enable_expert_parallel:
-                parallel_config.expert_tensor_parallel_size = 1
-            # NOTE: When enable_expert_parallel is False and param `asceend_config.expert_tensor_parallel_size`
-            # is configured, use ascend_config
-            elif ascend_config.expert_tensor_parallel_size > 0:
-                parallel_config.expert_tensor_parallel_size = ascend_config.expert_tensor_parallel_size
-
-            # Calculate expert parallel size based on world size
-            parallel_config.expert_parallel_size = (
-                parallel_config.world_size_across_dp //
-                parallel_config.expert_tensor_parallel_size)
-
         if model_config is None:
             logger.warning("Model config is missing. This may indicate "
                            "that we are running a test case")