[long_seq_optim] BSND to TND and FA_UPDATE replacement (#3778)

### What this PR does / why we need it?
We have optimized the performance of long sequences：First,Modify the
input data format for attention calculation. Instead of using the
original BSND format, remove the logic for converting between TND and
BSND, and directly adopt the TND format.
The TND input format can be directly reused, which shortens the data
flow path. Converting to BSND is an unnecessary processing step.Second,
we switched the output update of the concatenated small operators to the
npu_attention_update fusion operator to improve performance.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main:
c9461e05a4

---------

Signed-off-by: pichangping <1337510399@qq.com>

vllm_ascend/worker/model_runner_v1.py

@@ -1374,13 +1374,13 @@ class NPUModelRunner(LoRAModelRunnerMixin):
 
         self.input_batch.block_table.compute_slot_mapping(
             req_indices, positions_np)
+        self.input_batch.block_table.commit_slot_mapping(
+            total_num_scheduled_tokens)
         tokens, position_pcp, pcp_unpad_mask = self._update_tokens_for_pcp(
             tokens)
         num_scheduled_tokens = np.array(tokens, dtype=np.int32)
         # update total_num_scheduled_tokens
         total_num_scheduled_tokens = sum(num_scheduled_tokens[:num_reqs])
-        self.input_batch.block_table.commit_slot_mapping(
-            total_num_scheduled_tokens)
 
         total_num_pcp_pads = sum(self.num_pcp_pads)
         max_num_scheduled_tokens = max(tokens)
@@ -4140,7 +4140,6 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         num_decodes = sum(self.input_batch.num_computed_tokens_cpu[:num_reqs]
                           >= self.input_batch.num_prompt_tokens[:num_reqs])
         num_actual_tokens_pcp_padded = total_num_scheduled_tokens * self.pcp_size
-        num_prefills = num_reqs - num_decodes
         long_seq_metadata = None
         if self.pcp_size * self.dcp_size > 1:
             long_seq_metadata = AscendPrefillContextParallelMetadata(
@@ -4248,9 +4247,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                                    device=self.device,
                                    dtype=self.dtype), 1)
                 else:
-                    max_seq_len = max(seq_lens, default=0)
                     pcp_prefill_mask = torch.triu(
-                        torch.full((num_prefills, max_seq_len, max_seq_len),
+                        torch.full((2048, 2048),
                                    True,
                                    device=self.device,
                                    dtype=torch.bool), 1)