[refactor] refactor weight trans nz and transpose (#4878)

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

Now `VLLM_ASCEND_ENABLE_NZ` will have three options:
0: disable nz;
1: only quant case enable nz;
2: enable nz as long as possible;

And `VLLM_ASCEND_ENABLE_NZ`=1 by default.

All cases are shown in the table below:

|  | W4A4 | W4A8 | W8A8 | fp16/bf16 | fp32 |
|---|---|---|---|---|---|
| trans nz | can't support nz | trans nz by default | trans nz by
default | trans nz when VLLM_ASCEND_ENABLE_NZ is 2 | can't support nz |
| transpose | only support not transpose case | only support transpose
case | only support transpose case | linear: only support not transpose
case<br>gmm: only support transpose case | same to fp16/bf16 |

Some exceptional cases:
1. MLAPO op need to do some additional processing on the weights,
including trans nz. If use MLAPO op, some weight will be transformed to
nz forcely;
2. MLA/SFA's weight `W_UV` will be used by op
`torch.ops._C_ascend.batch_matmul_transpose`, and this op can't support
nz currently;

### Does this PR introduce _any_ user-facing change?
Now fp16/bf16 weight will not trans nz by default.

### How was this patch tested?

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

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

tests/ut/attention/test_mla_v1.py

@@ -4,7 +4,8 @@ from unittest.mock import MagicMock, patch
 import torch
 from vllm.config import CacheConfig, ModelConfig, SchedulerConfig, VllmConfig
 from vllm.distributed.parallel_state import GroupCoordinator
-from vllm.model_executor.layers.linear import LinearBase
+from vllm.model_executor.layers.linear import (LinearBase,
+                                               UnquantizedLinearMethod)
 
 from tests.ut.base import TestBase
 from vllm_ascend.ascend_config import init_ascend_config
@@ -972,16 +973,13 @@ class TestAscendMLAImpl(TestBase):
     def test_process_weights_after_loading(self, mock_format_cast):
         layer = MagicMock(spec=LinearBase)
         layer.input_size_per_partition = 10
-        quant_method = MagicMock()
-        apply = MagicMock()
-        quant_method.apply = apply
+        quant_method = MagicMock(spec=UnquantizedLinearMethod)
         layer.quant_method = quant_method
         shape_0 = self.impl.num_heads * (self.impl.qk_nope_head_dim +
                                          self.impl.v_head_dim)
         shape_1 = self.impl.kv_lora_rank
         layer.weight = torch.randn(shape_0, shape_1)
         self.impl.kv_b_proj = layer
-        apply.return_value = layer.weight.T
         mock_format_cast.return_value = layer.weight
         self.impl.process_weights_after_loading(torch.bfloat16)