[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/fused_moe/fused_moe.py

@@ -43,9 +43,9 @@ from vllm_ascend.quantization.w4a8_dynamic import \
     AscendW4A8DynamicFusedMoEMethod
 from vllm_ascend.quantization.w8a8_dynamic import \
     AscendW8A8DynamicFusedMoEMethod
-from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, enable_sp, is_310p,
-                               is_enable_nz, npu_stream_switch,
-                               shared_expert_dp_enabled,
+from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, AscendDeviceType,
+                               enable_sp, get_ascend_device_type, is_enable_nz,
+                               npu_stream_switch, shared_expert_dp_enabled,
                                shared_experts_calculation_stream)
 
 
@@ -79,7 +79,8 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
             w2_data = self._maybe_pad_weight(layer.w2_weight.data)
             layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
 
-        if not is_310p() and is_enable_nz():
+        if get_ascend_device_type() != AscendDeviceType._310P and is_enable_nz(
+        ):
             layer.w13_weight.data = torch_npu.npu_format_cast(
                 layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
             layer.w2_weight.data = torch_npu.npu_format_cast(