[Fix] Pads query_start_loc to satisfy FIA/TND constraint (#6357)

### What this PR does / why we need it?
This handles both uniform and mixed batches (by inserting a dummy
request for mixed batches), consolidates ad-hoc padding into a single
helper, copies the updated buffer to the device, and asserts the layout
constraint before building the attention metadata. Together, these
changes prevent kernel mismatches or failures and ensure correct shapes
for FIA/TND execution in full graph modes.

We currently place this helper in `execute_model`. My original design
was to include it in `_prepare_inputs`, but that doesn’t work because it
must run after padding. While I’d prefer to minimize the impact and
reuse as much of the base class as possible in the future, it doesn’t
seem achievable at the moment.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
Test cases added.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

tests/e2e/singlecard/test_aclgraph_accuracy.py

@@ -44,6 +44,29 @@ CASE_DS_ACLGRAPH = LLMTestCase(
     ],
 )
 
+CASE_QWEN_FULL = LLMTestCase(
+    model="Qwen/Qwen3-0.6B",
+    prompts=PROMPTS_SHORT,
+    golden_answers=[
+        " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I want to know if there are any",
+        ' the same as the president of the United Nations. This is because the president of the United States is the same as the president of the United Nations. The president',
+        ' Paris. The capital of France is also the capital of the Republic of France. The capital of France is also the capital of the European Union. The capital of',
+        ' not just a technological frontier but a profound transformation of how we live, work, and interact with the world. As we stand at the intersection of artificial intelligence and'
+    ],
+)
+
+CASE_DS_FULL = LLMTestCase(
+    model="vllm-ascend/DeepSeek-V2-Lite-W8A8",
+    quantization="ascend",
+    prompts=PROMPTS_SHORT,
+    golden_answers=[
+        '\nI am a 20 year old female, and I have been suffering from depression for 3 years now. I have been on medication for 2',
+        ' a man who has been in the public eye for decades. He has been a senator, a governor, and a businessman. He has also been married to the',
+        ' Paris, which is also the largest city in the country. The city is located on the River Seine and is known for its beautiful architecture, museums, and art',
+        ' here, and it’s not what you think.\nThe future of AI is here, and it’s not what you think.\nThe future of'
+    ],
+)
+
 CASE_QWEN_FULL_DECODE_ONLY = LLMTestCase(
     model="Qwen/Qwen3-0.6B",
     prompts=PROMPTS_LONG,
@@ -94,6 +117,23 @@ def test_piecewise_res_consistency(cur_case: LLMTestCase):
                   sampling_params=cur_case.sampling_params,
                   golden_answers=cur_case.golden_answers)
 
+@pytest.mark.parametrize(
+    "cur_case", [CASE_QWEN_FULL, CASE_DS_FULL])
+def test_full_res_consistency(cur_case: LLMTestCase, monkeypatch):
+    monkeypatch.delenv("HCCL_OP_EXPANSION_MODE", raising=False)
+    runner_kwargs = {
+        "model_name": cur_case.model,
+        "max_model_len": 1024,
+        "compilation_config": {
+            "cudagraph_capture_sizes": [4, 8, 32, 64],
+            "cudagraph_mode": "FULL_DECODE_ONLY"
+        },
+        "quantization": cur_case.quantization,
+    }
+    gen_and_valid(runner_kwargs=runner_kwargs,
+                  prompts=cur_case.prompts,
+                  sampling_params=cur_case.sampling_params,
+                  golden_answers=cur_case.golden_answers)
 
 @pytest.mark.parametrize(
     "cur_case", [CASE_QWEN_FULL_DECODE_ONLY, CASE_DS_FULL_DECODE_ONLY])