[Misc] Remove CP Redundant Variables after FIA operator enables for CANN 8.5 (#6013)

### What this PR does / why we need it?
PCP/DCP splits the kv-cache onto different cards. After introducing the
parameter cp-kv-cache-interleave-size, the first size tokens will be
cached at Card 0, and so on.
However, if there are too few tokens, some cards will not store the
key-value pairs, resulting in values ​​of 0, corrupted values, and
precision issues. Currently, additional operations are introduced to
avoid this precision problem.

After we integrate FIA operator in mla_cp._forward_decode and CANN
updates to 8.5.0, we now can remove these additional operations.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
passed all CI by CANN 8.5.0
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996

Signed-off-by: dsxsteven <dsxsteven@sina.com>
Signed-off-by: dsxsteven <36877507+dsxsteven@users.noreply.github.com>

tests/e2e/multicard/4-cards/long_sequence/test_accuracy.py

@@ -210,3 +210,72 @@ def test_accuracy_pcp_only(max_tokens: int, ) -> None:
         name_0="vllm_eager_outputs",
         name_1="vllm_pcp_only_outputs",
     )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [10])
+def test_models_long_sequence_cp_kv_interleave_size_output_between_tp_and_cp(
+    model: str,
+    max_tokens: int,
+) -> None:
+    prompts = [
+        "The president of the United States is", "The capital of France is"
+    ]
+
+    common_kwargs = {
+        "max_model_len": 1024,
+    }
+
+    if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
+        cp_kwargs = {
+            "tensor_parallel_size": 2,
+            "decode_context_parallel_size": 2,
+            "prefill_context_parallel_size": 2,
+            "enable_expert_parallel": True,
+            "cp_kv_cache_interleave_size": 128,
+            "enforce_eager": True,
+            "quantization": "ascend",
+        }
+        tp_kwargs = {
+            "tensor_parallel_size": 4,
+            "enable_expert_parallel": True,
+            "enforce_eager": True,
+            "quantization": "ascend",
+        }
+
+    else:
+        cp_kwargs = {
+            "tensor_parallel_size": 1,
+            "decode_context_parallel_size": 1,
+            "prefill_context_parallel_size": 2,
+            "cp_kv_cache_interleave_size": 128,
+            "compilation_config": {
+                "cudagraph_mode": "FULL_DECODE_ONLY",
+                "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+            },
+        }
+        tp_kwargs = {
+            "tensor_parallel_size": 2,
+            "enforce_eager": True,
+        }
+
+    cp_full_kwargs = {}
+    cp_full_kwargs.update(common_kwargs)  # type: ignore
+    cp_full_kwargs.update(cp_kwargs)  # type: ignore
+
+    tp_full_kwargs = {}
+    tp_full_kwargs.update(common_kwargs)  # type: ignore
+    tp_full_kwargs.update(tp_kwargs)  # type: ignore
+    with VllmRunner(model, **cp_full_kwargs) as runner:  # type: ignore
+        vllm_context_parallel_outputs = runner.generate_greedy(
+            prompts, max_tokens)
+
+    with VllmRunner(model, **tp_full_kwargs) as runner:  # type: ignore
+        vllm_eager_outputs = runner.generate_greedy(prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs,
+        outputs_1_lst=vllm_context_parallel_outputs,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_context_parallel_outputs",
+    )