[main][bugfix] Fixed the problem of speculative decoding in FULL mode (#7148)

### What this PR does / why we need it?

Fixed the error of speculative decoding in FULL mode when `num_spec + 1`
not in `cudagraph_capture_sizes`.

Now, we can run speculative decoding in FULL mode, but with drafter as
eager.

It depends on https://github.com/vllm-project/vllm-ascend/pull/7144 .

### Does this PR introduce _any_ user-facing change?

N/A

### How was this patch tested?

Test code is shown as below:

```python
prompts = [
    "1.Who are you?",
    "2. Who are you?",
]

sampling_params = SamplingParams(temperature=0.0, top_p=0.95, top_k=40, max_tokens=200)
llm = LLM(
    model="/home/some-model/Meta-Llama-3.1-8B-Instruct",
    tensor_parallel_size=1,
    max_num_seqs=32,
    # enforce_eager=True,
    disable_log_stats=False,
    distributed_executor_backend="mp",
    gpu_memory_utilization=0.7,
    async_scheduling=True,

    speculative_config={
        "enforce_eager": True,
        "model": "/home/some-model/EAGLE3-LLaMA3.1-Instruct-8B",
        "disable_padded_drafter_batch": False,
        "method": "eagle3",
        "num_speculative_tokens": 2,
    },
    
    compilation_config={
        "cudagraph_mode": "FULL",
        "cudagraph_num_of_warmups": 1,
    },

    max_model_len=4096, 
    enable_prefix_caching=False,
)

outputs = llm.generate(prompts, sampling_params)
```

The result before:

```text
   File "/vllm-workspace/vllm/vllm/v1/cudagraph_dispatcher.py", line 140, in _create_padded_batch_descriptor
     assert num_tokens_padded % uniform_decode_query_len == 0
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 AssertionError
```

The result after:

```text
--------------------------------------------------
total_num_output_tokens: 400
num_drafts: 249
num_draft_tokens: 498
num_accepted_tokens: 149
mean acceptance length: 1.60
--------------------------------------------------
acceptance at token 0: 0.43
acceptance at token 1: 0.17
```

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

Signed-off-by: drslark <slarksblood@qq.com>

vllm_ascend/spec_decode/eagle_proposer.py

@@ -89,7 +89,7 @@ class AscendEagleProposer(EagleProposer):
         self.use_async_scheduling = self.vllm_config.scheduler_config.async_scheduling
 
         self.decode_threshold = 1 + self.num_speculative_tokens
-        self.query_start_loc = self.runner._make_buffer(self.runner.max_num_reqs + 1, dtype=torch.int32)
+        self.query_start_loc = self.runner._make_buffer(self.runner.max_num_reqs + 2, dtype=torch.int32)
         self.arange_cpu = torch.arange(self.arange.shape[0], device="cpu", dtype=torch.int32)
         self.attn_mask_builder = AttentionMaskBuilder(self.device)
 
@@ -362,7 +362,9 @@ class AscendEagleProposer(EagleProposer):
 
         model_positions = self._get_positions(num_tokens)
 
-        batch_size = num_tokens // (self.num_speculative_tokens + 1)  # if not is_profile else self.runner.max_num_reqs
+        batch_size = max(
+            num_tokens // (self.num_speculative_tokens + 1), 1
+        )  # if not is_profile else self.runner.max_num_reqs
         if is_profile:
             batch_size = min(batch_size, self.runner.max_num_reqs)