[Refactor]refactor 310p ops and add ut (#6591)

### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
E2E test with qwen3-32b w8a8

- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

vllm_ascend/_310p/ops/mm_encoder_attention.py

@@ -15,7 +15,6 @@
 # This file is a part of the vllm-ascend project.
 #
 
-import einops
 import torch
 import torch_npu
 
@@ -37,31 +36,26 @@ class AscendMMEncoderAttention310(AscendMMEncoderAttention):
     ):
         bsz, q_len = query.size()[:2]
         kv_len = key.size(1)
-
-        q, k, v = self.reshape_qkv_to_3d(query, key, value, bsz, q_len, kv_len)
+        query = query.view(bsz * q_len, self.num_heads, self.head_size)
+        key = key.view(bsz * kv_len, self.num_kv_heads, self.head_size)
+        value = value.view(bsz * kv_len, self.num_kv_heads, self.head_size)
 
         if cu_seqlens is None:
-            cu_seqlens = torch.arange(
-                0,
-                (bsz + 1) * q_len,
-                step=q_len,
-                dtype=torch.int32,
-                device=query.device,
-            )
+            seq_len = torch.tensor([q_len] * bsz, device="cpu", dtype=torch.int32)
+        else:
+            seq_len = torch.diff(cu_seqlens.to("cpu", dtype=torch.int32))
 
-        seq_len = torch.diff(cu_seqlens).to("cpu", dtype=torch.int32)
-
-        context_layer = torch.empty_like(q)
+        output = torch.empty_like(query)
         torch_npu._npu_flash_attention_unpad(
-            query=q,
-            key=k,
-            value=v,
+            query=query,
+            key=key,
+            value=value,
             seq_len=seq_len,
             scale_value=self.head_size**-0.5,
             num_heads=self.num_heads,
             num_kv_heads=self.num_kv_heads,
-            out=context_layer,
+            out=output,
         )
 
-        context_layer = einops.rearrange(context_layer, "(b s) h d -> b s h d", b=bsz).contiguous()
-        return context_layer
+        output = output.view(bsz, -1, self.num_heads, self.head_size)
+        return output