[bugfix][ACLGraph][MTP] deletes cudagraph_batch_sizes in MtpProposer (#5183)

### What this PR does / why we need it?
This PR deletes `cudagraph_batch_sizes` in `MtpProposer` and reuses the
one in `NPUModelRunner`.

During our deployment of DeepSeek-V3.2 with MTP across machines 2P2D and
conducting AISBench stress testing, an error occurred (see below). After
investigation, we found that
`compilation_config.cudagraph_capture_sizes` is modified by
`adjust_cudagraph_sizes_for_spec_decode` in `NPUModelRunner`. This
modification only updates `cudagraph_batch_sizes` in `NPUModelRunner`
but is not synchronized to `MtpProposer`. After discussion (CC @yiz-liu)
, we believe it is unnecessary to maintain `cudagraph_batch_sizes` in
`MtpProposer`; it should directly use the variable from
`NPUModelRunner`.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>

tests/ut/spec_decode/test_mtp_proposer.py

@@ -97,7 +97,6 @@ class TestMtpProposer:
         proposer = MtpProposer(vllm_config, torch.device("cpu"), runner)
 
         assert proposer.use_aclgraph is True
-        assert proposer.cudagraph_batch_sizes == [1, 2, 4, 8]
 
     @patch("vllm.config.get_layers_from_vllm_config")
     @patch("vllm_ascend.spec_decode.mtp_proposer.get_model_loader")

vllm_ascend/spec_decode/mtp_proposer.py

@@ -107,11 +107,6 @@ class MtpProposer(Proposer):
 
         self.use_aclgraph = self.runner._use_aclgraph()
 
-        self.cudagraph_batch_sizes = (list(
-            sorted(
-                self.vllm_config.compilation_config.cudagraph_capture_sizes))
-                                      if self.use_aclgraph else [])
-
         # persistent buffers for aclgraph graph
         self.input_ids = torch.zeros(self.max_num_tokens,
                                      dtype=torch.int32,
@@ -697,11 +692,13 @@ class MtpProposer(Proposer):
 
         assert self.runner is not None
 
-        if self.runner.use_aclgraph and num_scheduled_tokens <= self.cudagraph_batch_sizes[
+        # Note(qcs): We may need to refactor these check logics.
+        if self.runner.use_aclgraph and num_scheduled_tokens <= self.runner.cudagraph_batch_sizes[
                 -1]:
             num_input_tokens = self.vllm_config.pad_for_cudagraph(
                 num_scheduled_tokens)
-        elif self.use_aclgraph and num_tokens <= self.cudagraph_batch_sizes[-1]:
+        elif self.use_aclgraph and num_tokens <= self.runner.cudagraph_batch_sizes[
+                -1]:
             # Acl graph mode, add padding to the batch size
             num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
         else: