support FULL_AND_PIECEWISE graph mode.

tests/e2e/multicard/test_full_graph_mode.py

@@ -29,7 +29,7 @@
 from tests.e2e.model_utils import check_outputs_equal
 
 
-def test_models_distributed_Qwen3_MOE_TP2_WITH_FULLGRAPH():
+def test_models_distributed_Qwen3_MOE_TP2_WITH_FULL_DECODE_ONLY():
     if 'HCCL_OP_EXPANSION_MODE' in os.environ:
         del os.environ['HCCL_OP_EXPANSION_MODE']
     prompts = [
@@ -70,3 +70,47 @@ def test_models_distributed_Qwen3_MOE_TP2_WITH_FULLGRAPH():
         name_0="vllm_eager_outputs",
         name_1="vllm_fullgraph_outputs",
     )
+
+
+def test_models_distributed_Qwen3_MOE_TP2_WITH_FULL_AND_PIECEIWSE():
+    if 'HCCL_OP_EXPANSION_MODE' in os.environ:
+        del os.environ['HCCL_OP_EXPANSION_MODE']
+    prompts = [
+        "Hello, my name is", "The president of the United States is",
+        "The capital of France is", "The future of AI is"
+    ]
+    model = "Qwen/Qwen3-30B-A3B"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            tensor_parallel_size=2,
+            enforce_eager=False,
+            compilation_config={"cudagraph_mode":
+                                "FULL_AND_PIECEIWSE"}) as runner:
+        vllm_fullgraph_outputs = runner.model.generate(prompts,
+                                                       sampling_params)
+
+    with VllmRunner(
+            model,
+            max_model_len=1024,
+            enforce_eager=True,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+
+    vllm_fullgraph_outputs_list = []
+    for output in vllm_fullgraph_outputs:
+        vllm_fullgraph_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=vllm_fullgraph_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_fullgraph_outputs",
+    )

vllm_ascend/compilation/acl_graph.py

@@ -332,7 +332,7 @@ def set_graph_params(aclgraph_capture_sizes: set[int]):
 def update_graph_params_workspaces(num_tokens: int, workspace: int):
     global _graph_params
     if _graph_params is not None:
-        _graph_params.workspaces[num_tokens] = workspace
+        _graph_params.workspaces[num_tokens] = weak_ref_tensors(workspace)
 
 
 def get_graph_params():

vllm_ascend/platform.py

@@ -223,16 +223,14 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
                 vllm_config.compilation_config.init_with_cudagraph_sizes(
                     sp_aclgraph_sizes)
 
-        # TODO: Full graph is fully supported later, and the default value will be set to full graph.
-        if compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
-            compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
-
         if compilation_config.cudagraph_mode == CUDAGraphMode.NONE:
             compilation_config.level = CompilationLevel.NO_COMPILATION
-        elif compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE:
+        elif compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE or \
+             compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE:
             logger.info(
-                "PIECEWISE compilation enabled on NPU. use_inductor not supported - "
-                "using only ACL Graph mode")
+                f"{compilation_config.cudagraph_mode} compilation enabled on NPU. use_inductor not supported - "
+                f"using only ACL Graph mode"
+                f"{compilation_config.cudagraph_mode}")
             assert compilation_config.level == CompilationLevel.PIECEWISE, \
                 "When enabling piecewise aclgraph, please make sure compilation_config.level == CompilationLevel.PIECEWISE and compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE"
             compilation_config.set_splitting_ops_for_v1()

vllm_ascend/torchair/torchair_model_runner.py

@@ -21,6 +21,7 @@
 import types
 from typing import Any, Optional
 
+import numpy as np
 import torch
 import torch.distributed as dist
 import torch.nn as nn
@@ -153,6 +154,7 @@ def _build_dummy_attn_metadata(
         num_reqs: int,
         num_tokens: int,
         max_query_len: int,
+        num_scheduled_tokens: np.ndarray[Any, Any],
         aclgraph_runtime_mode: Optional[CUDAGraphMode] = None,
         force_attention: bool = False,
     ) -> Optional[dict[str, Any]]:
@@ -161,7 +163,7 @@ 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

@@ -377,7 +377,8 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
                                         device=self.device)
 
         if self.vllm_config.model_config.use_mla and \
-            self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL_DECODE_ONLY:
+            (self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL_DECODE_ONLY or \
+             self.compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE):
             rope_dim = self.model_config.hf_text_config.qk_rope_head_dim
             self.cos = torch.ones(self.max_num_reqs *
                                   self.decode_token_per_req,
@@ -2260,6 +2261,7 @@ def _build_dummy_attn_metadata(
         num_reqs: int,
         num_tokens: int,
         max_query_len: int,
+        num_scheduled_tokens: np.ndarray[Any, Any],
         aclgraph_runtime_mode: Optional[CUDAGraphMode] = None,
         force_attention: bool = False,
     ) -> Optional[dict[str, Any]]:
@@ -2275,6 +2277,12 @@ def _build_dummy_attn_metadata(
             self.seq_lens_np[:num_reqs] = seq_lens
             self.seq_lens_np[num_reqs:] = 0
 
+            if num_scheduled_tokens is not None:
+                cu_num_tokens, arange = self._get_cumsum_and_arange(
+                    num_scheduled_tokens)
+                self.query_start_loc_cpu[1:num_reqs +
+                                         1] = torch.from_numpy(cu_num_tokens)
+
             num_computed_tokens_cpu = (
                 self.input_batch.num_computed_tokens_cpu_tensor[:num_reqs])
 
@@ -2474,6 +2482,7 @@ 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