[refact] unified soc_version code (#4359)

### What this PR does / why we need it?

Currently, there are two paths to judge the chip type in code,
`get_ascend_soc_version` use `get_soc_version` api in torch_npu, and
`is_310p` `use _build_info.__soc_version__`, which generate when
install. We need to unify the two paths.

We need to unify these codes based on the following points:

1. We need to ensure consistency in chip type judgment between compiling
and running states;
2. In compiling state, we need chip type to complete op's compilation,
but in running state, we only need device
type(910B/910_93/310P/910_95/etc) to make code branch judgement;
3. In compiling state, torch_npu may not have been installed yet, so we
can't use torch_npu's api.

Based on the above points, we have made the following changes:

1. When user set env `SOC_VERSION`, use it; when not set, query
soc_version by `npu-smi`;
2. generate device_type based on soc_version when compiling, and write
`__device_type__` instead of `__soc_version__` in `_build_info.py`;
3. In running state, use `__device_type__` to judge code branch.

### Does this PR introduce _any_ user-facing change?

When not set env `SOC_VERSION`, it will not be `ASCEND910B1` by default,
we will query soc_version by `npu-smi`. And env `SOC_VERSION` must be in
the list `soc_to_device` in `setup.py`.

- vLLM version: v0.11.0
- vLLM main:
2918c1b49c

Signed-off-by: zzzzwwjj <1183291235@qq.com>

vllm_ascend/ops/layernorm.py

@@ -32,9 +32,10 @@ def _addrmsnorm_forward_oot(
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
     import torch_npu
 
-    from vllm_ascend.utils import is_310p
+    from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
 
-    if layer is not None and not is_310p():
+    if layer is not None and get_ascend_device_type(
+    ) != AscendDeviceType._310P:
         layer_cls_name = layer.__class__.__name__
         try:
             weight_prefetch_method = get_forward_context(
@@ -67,7 +68,7 @@ def _addrmsnorm_forward_oot(
             )
 
     else:
-        if is_310p():
+        if get_ascend_device_type() == AscendDeviceType._310P:
             orig_dtype = residual.dtype
             x = x + residual.to(x.dtype)
             residual = x.to(orig_dtype)
@@ -195,9 +196,9 @@ class AscendGemmaRMSNorm(GemmaRMSNorm):
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         import torch_npu
 
-        from vllm_ascend.utils import is_310p
+        from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
         if residual is not None:
-            if is_310p():
+            if get_ascend_device_type() == AscendDeviceType._310P:
                 orig_dtype = residual.dtype
                 x = x + residual.to(x.dtype)
                 residual = x.to(orig_dtype)