[Feat]mtp aclgraph support (#3244)

### What this PR does / why we need it?
Currently, MTP Model in deepseek can not be capture in ACLGraph. This PR
is use to allow MTP to be captured in ACLGraph mode.

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

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>

vllm_ascend/models/deepseek_mtp.py

@@ -23,6 +23,7 @@ import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
 from vllm.attention.backends.abstract import AttentionMetadata
+from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
                          get_current_vllm_config)
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -179,6 +180,7 @@ class CustomDeepSeekMultiTokenPredictor(DeepSeekMultiTokenPredictor):
         return logits
 
 
+@support_torch_compile
 class CustomDeepSeekMTP(DeepSeekMTP):
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

vllm_ascend/spec_decode/eagle_proposer.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from vllm.attention.layer import Attention
-from vllm.config import (CompilationLevel, VllmConfig,
+from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig,
                          get_layers_from_vllm_config)
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import logger
@@ -114,7 +114,9 @@ class EagleProposer(Proposer):
                   with_prefill: bool = False,
                   skip_attn: bool = False,
                   num_reqs: int = 0,
-                  num_tokens_across_dp: Optional[torch.Tensor] = None):
+                  num_tokens_across_dp: Optional[torch.Tensor] = None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None):
         moe_comm_type = self.runner._select_moe_comm_method(
             num_tokens, with_prefill)
         with set_ascend_forward_context(None,

vllm_ascend/spec_decode/interface.py

@@ -2,7 +2,7 @@ import enum
 from typing import Optional
 
 import torch
-from vllm.config import VllmConfig
+from vllm.config import CUDAGraphMode, VllmConfig
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
@@ -33,7 +33,9 @@ class Proposer:
                   with_prefill: bool = False,
                   skip_attn: bool = False,
                   num_reqs: int = 0,
-                  num_tokens_across_dp: Optional[torch.Tensor] = None):
+                  num_tokens_across_dp: Optional[torch.Tensor] = None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None):
         """Called by dummy_run in modle_runner"""
         raise NotImplementedError
 

vllm_ascend/spec_decode/mtp_proposer.py

@@ -5,8 +5,8 @@ import torch.nn as nn
 import torchair
 from torchair import patch_for_hcom
 from vllm.attention.layer import Attention
-from vllm.config import (VllmConfig, get_layers_from_vllm_config,
+from vllm.config import (CUDAGraphMode, VllmConfig,
-                         set_current_vllm_config)
+                         get_layers_from_vllm_config, set_current_vllm_config)
 from vllm.forward_context import BatchDescriptor, get_forward_context
 from vllm.model_executor.model_loader import get_model_loader
 from vllm.model_executor.model_loader.utils import (
@@ -109,7 +109,9 @@ class MtpProposer(Proposer):
                   with_prefill: bool = False,
                   skip_attn: bool = False,
                   num_reqs: int = 0,
-                  num_tokens_across_dp=None) -> None:
+                  num_tokens_across_dp=None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None) -> None:
         if not self.torchair_graph_enabled:
             # TODO: adapt enable_dbo later
             (num_tokens, num_tokens_across_dp, with_prefill,
@@ -151,7 +153,9 @@ class MtpProposer(Proposer):
                     reserved_mc2_mask=self.runner.reserved_mc2_mask,
                     moe_comm_type=moe_comm_type,
                     in_profile_run=self.runner.in_profile_run,
-                    num_actual_tokens=0):
+                    num_actual_tokens=0,
+                    aclgraph_runtime_mode=aclgraph_runtime_mode,
+                    batch_descriptor=batch_descriptor):
                 if is_running_torchair:
                     assert attn_metadata is not None
                     torch._dynamo.mark_static(input_ids)
@@ -442,6 +446,7 @@ class MtpProposer(Proposer):
                     reserved_mc2_mask=self.runner.reserved_mc2_mask,
                     moe_comm_type=moe_comm_type,
                     aclgraph_runtime_mode=aclgraph_runtime_mode,
+                    batch_descriptor=batch_descriptor,
                     in_profile_run=self.runner.in_profile_run,
                     num_actual_tokens=num_tokens):
                 with ProfileExecuteDuration().capture_async('mtp_forward'):

vllm_ascend/spec_decode/ngram_proposer.py

@@ -1,4 +1,5 @@
 import torch
+from vllm.config import CUDAGraphMode
 from vllm.v1.spec_decode.ngram_proposer import \
     NgramProposer as VllmNgramProposer
 
@@ -23,7 +24,9 @@ class NgramProposer(VllmNgramProposer, Proposer):
                   with_prefill=None,
                   skip_attn=None,
                   num_reqs=None,
-                  num_tokens_across_dp=None):
+                  num_tokens_across_dp=None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None):
         pass
 
     def generate_token_ids(self,

vllm_ascend/worker/model_runner_v1.py

@@ -2479,7 +2479,9 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                     with_prefill=with_prefill,
                     skip_attn=True,
                     num_reqs=num_reqs,
-                    num_tokens_across_dp=num_tokens_across_dp)
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    aclgraph_runtime_mode=aclgraph_runtime_mode,
+                    batch_descriptor=batch_descriptor)
                 if need_dummy_logits:
                     dummy_compute_logits(hidden_states)
             if self.in_profile_run and self.dynamic_eplb: