[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>

tests/ut/ops/test_activation.py

@@ -19,6 +19,8 @@ import pytest
 import torch
 from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
 
+from vllm_ascend.utils import AscendDeviceType
+
 
 @pytest.fixture
 def dummy_tensor():
@@ -36,20 +38,22 @@ def test_QuickGELU_forward(mock_gelu, dummy_tensor):
     mock_gelu.assert_called_once()
 
 
-@pytest.mark.parametrize("is_310p_return", [True, False])
+@pytest.mark.parametrize("is_310p", [True, False])
 @patch("torch_npu.npu_swiglu", side_effect=lambda x: x + 1)
 @patch("torch.ops.vllm.maybe_wait_prefetch_done", side_effect=lambda x: None)
 @patch("torch.ops.vllm.maybe_prefetch_mlp_down_proj",
        side_effect=lambda x: None)
 def test_SiluAndMul_forward(mock_maybe_prefetch_mlp_down_proj,
                             mock_maybe_wait_prefetch_done, mock_swiglu,
-                            is_310p_return, dummy_tensor):
+                            is_310p, dummy_tensor):
 
-    with patch("vllm_ascend.utils.is_310p", return_value=is_310p_return):
+    with patch("vllm_ascend.utils.get_ascend_device_type",
+               return_value=AscendDeviceType._310P
+               if is_310p else AscendDeviceType._910_93):
         layer = SiluAndMul()
         out = layer.forward(dummy_tensor)
 
-        if is_310p_return:
+        if is_310p:
             expected_arg = dummy_tensor.to(torch.float32)
         else:
             expected_arg = dummy_tensor