[V1] MTP supports torchair (#2145)

### What this PR does / why we need it?
Support MTP  with：

- [x]  V0 Scheduler
- [x]  TorchAir
- [x]  Single DP
- [x]  Multi DP
- [x]  Disaggregate PD

Known issues：
- [ ] Not support V1 Scheduler (chunked prefill), will be supported in a
few weeks
- [ ] vllm v0.10.0 does not support metrics with `DP > 1` right now,
need to comment out the line 171-175 in file
`vllm/vllm/v1/metrics/loggers.py`
```
            if (len(self.engine_indexes) > 1
                and vllm_config.speculative_config is not None):
            raise NotImplementedError("Prometheus metrics with Spec Decoding "
                                      "with >1 EngineCore per AsyncLLM is not "
                                      "supported yet.")
```

To start an online server with torchair enabled, here is an example:
```
python -m vllm.entrypoints.openai.api_server \
 --model="/weights/DeepSeek-R1_w8a8/" \
 --trust-remote-code \
 --max-model-len 40000 \
 --tensor-parallel-size 4 \
 --data_parallel_size 4 \
 --max-num-seqs 16 \
 --no-enable-prefix-caching \
 --enable_expert_parallel \
 --served-model-name deepseekr1 \
 --speculative-config '{"num_speculative_tokens": 1, "method":"deepseek_mtp"}' \
 --quantization ascend \
 --host 0.0.0.0 \
 --port 1234 \
 --additional-config '{"ascend_scheduler_config":{"enabled":true,"enable_chunked_prefill":false},"torchair_graph_config":{"enabled":true,"graph_batch_sizes":[16]},"enable_weight_nz_layout":true}' \
 --gpu_memory_utilization 0.9 
``` 

offline example with torchair enabled
```
from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=16, temperature=0)
# Create an LLM.
llm = LLM(
    model="/home/data/DeepSeek-R1_w8a8/",
    tensor_parallel_size=16,
    max_num_seqs=16,
    gpu_memory_utilization=0.9,
    distributed_executor_backend="mp",
    enable_expert_parallel=True,
    speculative_config={
        "method": "deepseek_mtp",
        "num_speculative_tokens": 1,
    },
    trust_remote_code=True,
    enforce_eager=False,
    max_model_len=2000,
    additional_config = {
       'torchair_graph_config': {
            'enabled': True,
            "graph_batch_sizes": [16],
            'enable_multistream_shared_expert': False,
        },
       "ascend_scheduler_config": {
            "enabled": True
        },
        # 'expert_tensor_parallel_size': 16,
    }
)

# Generate texts from the prompts.
# llm.start_profile()
outputs = llm.generate(prompts, sampling_params)
# llm.stop_profile()
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```

- vLLM version: v0.10.0
- vLLM main:
302962e806

---------

Signed-off-by: xuyexiong <xuyexiong@huawei.com>

vllm_ascend/attention/attention_v1_torchair.py

@@ -156,7 +156,7 @@ class AscendAttentionTorchairMetadataBuilder:
             self, num_seqs: int, block_tables: torch.Tensor) -> torch.Tensor:
 
         max_batch_size, max_blocks = self.runner.graph_block_tables.shape
-        assert max_batch_size >= num_seqs
+        assert max_batch_size >= num_seqs, f"max_batch_size: {max_batch_size} should be bigger than cur_num_seqs: {num_seqs}"
 
         if isinstance(self.runner.graph_block_tables, np.ndarray):
             graph_block_tables = torch.zeros((max_batch_size, max_blocks),
@@ -259,26 +259,34 @@ class AscendAttentionTorchairMetadataBuilder:
             if use_torchair_graph and self.runner.attn_state in [
                     AscendAttentionState.DecodeOnly,
             ]:
+                num_reqs_pad_size = 0
+                num_token_pad_size = 0
+                if graph_pad_size != 0:
+                    pad_value = 0
+                    num_token_pad_size = graph_pad_size - num_actual_tokens
+                    num_reqs_pad_size = (
+                        graph_pad_size // self.runner.decode_token_per_req -
+                        num_reqs)
                 pad_value = 1
                 padded_seq_lens = seq_lens.tolist() + [pad_value
-                                                       ] * graph_pad_size
+                                                       ] * num_reqs_pad_size
 
                 seq_lens = torch.from_numpy(
                     np.array(padded_seq_lens).astype(np.int32))
-                padding = torch.full((graph_pad_size, ),
+                padding = torch.full((num_token_pad_size, ),
                                      PAD_SLOT_ID,
                                      dtype=slot_mapping.dtype,
                                      device=slot_mapping.device)
                 slot_mapping = torch.cat([slot_mapping, padding])
                 block_table_padding = torch.zeros(
-                    (graph_pad_size, ) + block_table.shape[1:],
+                    (num_reqs_pad_size, ) + block_table.shape[1:],
                     dtype=block_table.dtype,
                     device=block_table.device)
                 block_table = torch.cat([block_table, block_table_padding],
                                         dim=0)
                 block_table = self._get_graph_runner_block_tables(
-                    num_seqs + graph_pad_size, block_table)
-                padding_0 = torch.zeros(graph_pad_size,
+                    num_seqs + num_reqs_pad_size, block_table)
+                padding_0 = torch.zeros(num_token_pad_size,
                                         dtype=input_positions.dtype,
                                         device=input_positions.device)
                 input_positions = torch.cat([input_positions, padding_0])