[Bugfix] Fix the input constraints checks for the mlapo and bmm_transpose operators (#5764)

### What this PR does / why we need it?
This PR fix the input constraints checks for the mlapo and bmm_transpose
operators.

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

### How was this patch tested?
CI passed with new added/existing test.

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

### Perf
64K/3K，1P1D，bs=32

before this pr:
TPOT 29ms, TTFT 47s，TPS 606 token/s

after this pr:
TPOT 29ms, TTFT 48s，TPS 636 token/s

Signed-off-by: rjg-lyh <1318825571@qq.com>

vllm_ascend/attention/mla_v1.py

@@ -57,6 +57,8 @@ else:
 MAX_O_PROJ_PREFETCH_SIZE = 16 * 1024 * 1024
 BUILD_METADATA_STEP_PREFILL = 0
 BUILD_METADATA_STEP_DECODE = 1
+# token count limits within the mlapo operator
+MLAPO_MAX_SUPPORTED_TOKENS = 1024
 
 
 class AscendMLABackend(AttentionBackend):
@@ -927,10 +929,9 @@ class AscendMLAImpl(MLAAttentionImpl):
         # On KV consumers (decode-only) MLAPO uses the transformed weights built above;
         # the original fused_qkv_a_proj/q_proj weights and quant params are no longer
         # referenced, so drop them to save memory.
-        ascend_config = get_ascend_config()
         if self.vllm_config.kv_transfer_config is not None and \
                 self.vllm_config.kv_transfer_config.is_kv_consumer and \
-                ascend_config.recompute_scheduler_enable:
+                self.vllm_config.scheduler_config.max_num_batched_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
             self.fused_qkv_a_proj.weight = None
             self.fused_qkv_a_proj.deq_scale = None
             self.fused_qkv_a_proj.quant_bias = None
@@ -1508,7 +1509,9 @@ class AscendMLAImpl(MLAAttentionImpl):
                                    device=hidden_states.device)
 
         # MLA Preprocess
-        if self.enable_mlapo and not has_prefill:
+        if self.enable_mlapo and \
+            not has_prefill and \
+            attn_metadata.num_decode_tokens <= MLAPO_MAX_SUPPORTED_TOKENS:
             hidden_states = torch.ops.vllm.maybe_all_gather_and_maybe_unpad(
                 hidden_states.contiguous(), need_gather_q_kv)
             decode_preprocess_res, prefill_preprocess_res = self._mla_preprocess_only_decode(