support qwen3-next full_decode_only mode.

tests/e2e/multicard/test_qwen3_next.py

@@ -36,3 +36,21 @@ def test_models_distributed_Qwen3_NEXT_TP4():
                     distributed_executor_backend="mp",
                     enforce_eager=True) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_NEXT_TP4_FULL_DECODE_ONLY():
+    example_prompts = [
+        "Hello, my name is",
+    ] * 4
+    max_tokens = 5
+    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
+                    tensor_parallel_size=4,
+                    max_model_len=4096,
+                    gpu_memory_utilization=0.8,
+                    distributed_executor_backend="mp",
+                    enforce_eager=False,
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [1, 8, 24, 48, 60]
+                    }) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

vllm_ascend/compilation/acl_graph.py

@@ -192,8 +192,10 @@ def __call__(self, *args, **kwargs):
 
 def update_attn_params(update_stream, forward_context, runtime_shape):
     graph_params = get_graph_params()
-    # FIXME: Behold! We are using a temporary hack here to update the args
-    # for each layer's attention op in the graph.
+    # For Qwen3-next, since the kv_cache_config has already categorized
+    # linear_attn and self_attn, the attn_metadata is first arranged with
+    # self_attn followed by linear_attn. Therefore, using zip directly
+    # filters out the update operations for linear_attn.
     with torch.npu.stream(update_stream):
         for key, param, handle, event in zip(
                 forward_context.attn_metadata,
@@ -289,9 +291,9 @@ def update_mla_attn_params(update_stream, forward_context, runtime_shape,
 
 
 def update_attn_dcp_pcp_params(update_stream, forward_context, runtime_shape):
-    graph_params = get_graph_params()
     # FIXME: Behold! We are using a temporary hack here to update the args
     # for each layer's attention op in the graph.
+    graph_params = get_graph_params()
     with torch.npu.stream(update_stream):
         for key, param, handle, event in zip(
                 forward_context.attn_metadata,

vllm_ascend/torchair/torchair_model_runner.py

@@ -31,6 +31,7 @@
 from vllm.forward_context import get_forward_context
 from vllm.logger import logger
 
+import numpy as np
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.platform import NPUPlatform
@@ -178,6 +179,7 @@ def _build_dummy_attn_metadata(
         num_reqs: int,
         num_tokens: int,
         max_query_len: int,
+        num_scheduled_tokens: np.ndarray,
         aclgraph_runtime_mode: Optional[CUDAGraphMode] = None,
         force_attention: bool = False,
     ) -> Optional[dict[str, Any]]:
@@ -186,7 +188,7 @@ def _build_dummy_attn_metadata(
         if with_prefill or self.enable_shared_expert_dp:
             attn_metadata = super()._build_dummy_attn_metadata(
                 with_prefill, num_reqs, num_tokens, max_query_len,
-                aclgraph_runtime_mode, force_attention)
+                num_scheduled_tokens, aclgraph_runtime_mode, force_attention)
         else:
             common_attn_metadata = TorchairCommonAttentionMetadata(
                 num_reqs=num_reqs,

vllm_ascend/worker/model_runner_v1.py

@@ -76,7 +76,8 @@
 from vllm.utils.jsontree import json_map_leaves
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
-    AttentionCGSupport, reorder_batch_to_split_decodes_and_prefills)
+    AttentionCGSupport, CommonAttentionMetadata,
+    reorder_batch_to_split_decodes_and_prefills)
 from vllm.v1.cudagraph_dispatcher import CudagraphDispatcher
 # yapf conflicts with isort for this block
 # yapf: disable
@@ -107,7 +108,8 @@
 from vllm_ascend.ascend_forward_context import (MoECommType,
                                                 set_ascend_forward_context)
 from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
-from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm_ascend.attention.attention_v1 import (AscendAttentionMetadataBuilder,
+                                                AscendAttentionState)
 from vllm_ascend.attention.utils import (AscendCommonAttentionMetadata,
                                          AscendPrefillContextParallelMetadata)
 # yapf: disable
@@ -2651,6 +2653,7 @@ def _build_dummy_attn_metadata(
         num_reqs: int,
         num_tokens: int,
         max_query_len: int,
+        num_scheduled_tokens: np.ndarray,
         aclgraph_runtime_mode: Optional[CUDAGraphMode] = None,
         force_attention: bool = False,
     ) -> Optional[dict[str, Any]]:
@@ -2666,6 +2669,14 @@ def _build_dummy_attn_metadata(
             self.seq_lens_np[:num_reqs] = seq_lens
             self.seq_lens_np[num_reqs:] = 0
 
+            cu_num_tokens, arange = self._get_cumsum_and_arange(
+                num_scheduled_tokens)
+            query_start_loc_tensor = torch.Tensor(cu_num_tokens).to(
+                self.device).to(torch.int32)
+            self.query_start_loc[1:num_reqs + 1] = query_start_loc_tensor
+            self.query_start_loc_cpu[1:num_reqs +
+                                     1] = torch.Tensor(cu_num_tokens)
+
             num_computed_tokens_cpu = (
                 self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs])
 
@@ -2722,12 +2733,35 @@ def _build_dummy_attn_metadata(
                         self.speculative_config.method == "deepseek_mtp":
                     attn_state = AscendAttentionState.SpecDecoding
 
+                common_metadata = CommonAttentionMetadata(
+                    query_start_loc=self.query_start_loc[:num_reqs + 1],
+                    query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs +
+                                                                 1],
+                    seq_lens_cpu=self.seq_lens_cpu[:num_reqs],
+                    seq_lens=self.seq_lens_cpu[:num_reqs],
+                    num_reqs=num_reqs,
+                    num_actual_tokens=num_tokens,
+                    block_table_tensor=block_table_tensor[:num_reqs],
+                    slot_mapping=slot_mapping,
+                    num_computed_tokens_cpu=num_computed_tokens_cpu,
+                    max_query_len=max_query_len,
+                    max_seq_len=seq_lens)
+
                 for attn_group in self.attn_groups[kv_cache_group_id]:
                     builder = attn_group.get_metadata_builder()
-                    attn_metadata_i = builder.build_for_graph_capture(
-                        common_attn_metadata, attn_state, self.get_model())
+                    if isinstance(builder, AscendAttentionMetadataBuilder):
+                        attn_metadata_full_attention = builder.build_for_graph_capture(
+                            common_attn_metadata, attn_state, self.get_model())
+                    elif isinstance(builder, GDNAttentionMetadataBuilder):
+                        attn_metadata_gdn_attention = builder.build_for_cudagraph_capture(
+                            common_metadata)
                     for layer_name in kv_cache_group_spec.layer_names:
-                        attn_metadata[layer_name] = attn_metadata_i
+                        if "linear_attn" in layer_name:
+                            attn_metadata[
+                                layer_name] = attn_metadata_gdn_attention
+                        else:
+                            attn_metadata[
+                                layer_name] = attn_metadata_full_attention
 
         return attn_metadata
 
@@ -2902,6 +2936,7 @@ def _dummy_run(
                 max_query_len=max_query_len,
                 aclgraph_runtime_mode=aclgraph_runtime_mode,
                 force_attention=force_attention,
+                num_scheduled_tokens=num_scheduled_tokens,
             )
 
             need_dummy_logits = (not self.in_profile_run