[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/sample/sampler.py

@@ -3,7 +3,7 @@ import torch_npu
 from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler, random_sample
 from vllm.v1.sample.sampler import Sampler
 
-from vllm_ascend.utils import is_310p
+from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
 
 DEFAULT_LOGPROBS_MODE = "raw_logprobs"
 
@@ -25,7 +25,8 @@ class AscendTopKTopPSampler(TopKTopPSampler):
         p: torch.Tensor,
     ) -> torch.Tensor:
         # npu_top_k_top_p uses the operator aclnnApplyTopKTopP, but aclnnApplyTopKTopP currently does not support 310P
-        if not is_310p() and p is not None and k is not None and 1 <= int(
+        if get_ascend_device_type(
+        ) != AscendDeviceType._310P and p is not None and k is not None and 1 <= int(
                 k.max()) <= 1024:
             # npu_top_k_top_p's parameter order is (logits, p, k), not (logits, k, p)
             return torch_npu.npu_top_k_top_p(logits, p, k)