[Feat][UT] Support Deepseekv32 FULL_DECODE_ONLY mode and add unit test of sfa_v1 (#3763)

### What this PR does / why we need it?
- Add support for DeepSeek v3.2 in FULL_DECODE_ONLY mode.
- Add unit test for sfa_v1.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?


- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: 1Fire4 <wangdingyi2@huawei.com>

tests/ut/attention/test_sfa_v1.py

@@ -0,0 +1,185 @@
+from unittest.mock import MagicMock
+
+import torch
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+
+from tests.ut.base import TestBase
+from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm_ascend.attention.sfa_v1 import (AscendSFABackend, AscendSFAImpl,
+                                          AscendSFAMetadata,
+                                          AscendSFAMetadataBuilder)
+
+
+class TestAscendSFABackend(TestBase):
+
+    def test_get_name(self):
+        self.assertEqual(AscendSFABackend.get_name(), "ASCEND_SFA")
+
+    def test_get_metadata_cls(self):
+        self.assertEqual(AscendSFABackend.get_metadata_cls(),
+                         AscendSFAMetadata)
+
+    def test_get_builder_cls(self):
+        self.assertEqual(AscendSFABackend.get_builder_cls(),
+                         AscendSFAMetadataBuilder)
+
+    def test_get_kv_cache_shape(self):
+        result = AscendSFABackend.get_kv_cache_shape(2, 4, 8, 128)
+        self.assertEqual(result, (2, 4, 8, 128))
+
+    def test_get_impl_cls(self):
+        result = AscendSFABackend.get_impl_cls()
+        self.assertEqual(result, AscendSFAImpl)
+
+
+class TestAscendSFAMetadata(TestBase):
+
+    def test_ascend_sfa_metadata_default(self):
+        has_prefill = True
+        num_actual_tokens = 100
+        slot_mapping = torch.randn(100, 4, 1024)
+        seq_lens = torch.tensor([30, 50])
+        cum_query_lens = torch.tensor([0, 30, 80])
+        block_tables = torch.randint(0, 100, (100, 4))
+
+        rope_dim = 32
+        max_seq_len = int(seq_lens.max().item())
+        sin = torch.randn(max_seq_len, rope_dim)
+        cos = torch.randn(max_seq_len, rope_dim)
+
+        num_input_tokens = 2
+        head_dim = None
+        attn_mask = None
+        attn_state = AscendAttentionState.ChunkedPrefill
+
+        metadata = AscendSFAMetadata(
+            has_prefill=has_prefill,
+            num_actual_tokens=num_actual_tokens,
+            slot_mapping=slot_mapping,
+            seq_lens=seq_lens,
+            cum_query_lens=cum_query_lens,
+            block_tables=block_tables,
+            sin=sin,
+            cos=cos,
+            num_input_tokens=num_input_tokens,
+            head_dim=head_dim,
+            attn_mask=attn_mask,
+            attn_state=attn_state,
+        )
+
+        self.assertEqual(metadata.has_prefill, has_prefill)
+        self.assertEqual(metadata.num_actual_tokens, num_actual_tokens)
+        self.assertIs(metadata.slot_mapping, slot_mapping)
+        self.assertTrue(torch.equal(metadata.seq_lens, seq_lens))
+        self.assertTrue(torch.equal(metadata.cum_query_lens, cum_query_lens))
+        self.assertIs(metadata.block_tables, block_tables)
+        self.assertIs(metadata.sin, sin)
+        self.assertIs(metadata.cos, cos)
+        self.assertEqual(metadata.num_input_tokens, num_input_tokens)
+        self.assertIs(metadata.head_dim, head_dim)
+        self.assertIs(metadata.attn_mask, attn_mask)
+        self.assertEqual(metadata.attn_state, attn_state)
+
+
+class TestAscendSFAMetadataBuilder(TestBase):
+
+    def test_ascend_sfa_metadata_builder_default(self):
+        kv_cache_spec = MagicMock()
+        layer_names = ["layer1", "layer2"]
+        vllm_config = MagicMock()
+        speculative_config = MagicMock()
+        speculative_config.num_speculative_tokens = 4
+        vllm_config.speculative_config = speculative_config
+        device = torch.device("cpu")
+
+        builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
+                                           layer_names=layer_names,
+                                           vllm_config=vllm_config,
+                                           device=device)
+
+        assert builder.aclgraph_support == AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+        assert builder.device == device
+        assert builder.vllm_config == vllm_config
+
+    def test_ascend_sfa_metadata_builder_build(self):
+        kv_cache_spec = MagicMock()
+        layer_names = ["layer1", "layer2"]
+        vllm_config = MagicMock()
+        speculative_config = MagicMock()
+        speculative_config.num_speculative_tokens = 4
+        vllm_config.speculative_config = speculative_config
+        device = torch.device("cpu")
+
+        builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
+                                           layer_names=layer_names,
+                                           vllm_config=vllm_config,
+                                           device=device)
+
+        common_attn_metadata = MagicMock()
+        common_attn_metadata.num_reqs = 10
+        common_attn_metadata.num_actual_tokens = 100
+        common_attn_metadata.query_start_loc = torch.tensor(
+            [0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
+        common_attn_metadata.query_start_loc_cpu = torch.tensor(
+            [0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
+        common_attn_metadata.slot_mapping = torch.randn(100, 4, 1024)
+        common_attn_metadata.seq_lens_cpu = torch.tensor([2] * 10)
+        common_attn_metadata.positions = torch.randn(100)
+        common_attn_metadata.attn_mask = None
+        common_attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
+        common_attn_metadata.block_table_tensor = torch.randn(100, 4)
+
+        model = MagicMock()
+        model.model.layers = [MagicMock() for _ in range(10)]
+        model.model.start_layer = 0
+
+        metadata = builder.build(
+            common_prefix_len=10,
+            common_attn_metadata=common_attn_metadata,
+            model=model,
+        )
+
+        assert isinstance(metadata, AscendSFAMetadata)
+        assert metadata.num_actual_tokens == common_attn_metadata.num_actual_tokens
+        assert metadata.slot_mapping.shape == (100, 4, 1024)
+
+    def test_ascend_sfa_metadata_builder_build_for_graph_capture(self):
+        kv_cache_spec = MagicMock()
+        layer_names = ["layer1", "layer2"]
+        vllm_config = MagicMock()
+        speculative_config = MagicMock()
+        speculative_config.num_speculative_tokens = 4
+        vllm_config.speculative_config = speculative_config
+        device = torch.device("cpu")
+
+        builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
+                                           layer_names=layer_names,
+                                           vllm_config=vllm_config,
+                                           device=device)
+
+        common_attn_metadata = MagicMock()
+        common_attn_metadata.num_reqs = 10
+        common_attn_metadata.num_actual_tokens = 100
+        common_attn_metadata.query_start_loc = torch.tensor(
+            [0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
+        common_attn_metadata.query_start_loc_cpu = torch.tensor(
+            [0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
+        common_attn_metadata.slot_mapping = torch.randn(100, 4, 1024)
+        common_attn_metadata.seq_lens_cpu = torch.tensor([2] * 10)
+        common_attn_metadata.positions = torch.randn(100)
+        common_attn_metadata.attn_mask = None
+        common_attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
+        common_attn_metadata.block_table_tensor = torch.randn(100, 4)
+
+        model = MagicMock()
+        model.model.layers = [MagicMock() for _ in range(10)]
+        model.model.start_layer = 0
+
+        attn_metadata = builder.build_for_graph_capture(
+            common_attn_metadata=common_attn_metadata,
+            attn_state=AscendAttentionState.DecodeOnly,
+            model=model,
+        )
+
+        assert isinstance(attn_metadata, AscendSFAMetadata)
+        assert attn_metadata.attn_state == AscendAttentionState.DecodeOnly

vllm_ascend/attention/sfa_v1.py

@@ -91,7 +91,7 @@ M = TypeVar("M", bound=AscendSFAMetadata)
 class AscendSFAMetadataBuilder:
     # Does this backend/builder support ACL Graphs for attention (default: no).
     aclgraph_support: ClassVar[AttentionCGSupport] = \
-        AttentionCGSupport.NEVER
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
     """
     NOTE: Please read the comment at the top of the file before trying to
     understand this class
@@ -189,6 +189,26 @@ class AscendSFAMetadataBuilder:
             sin=sin,
             cos=cos)
 
+    def build_for_graph_capture(
+        self,
+        common_attn_metadata: AscendCommonAttentionMetadata,
+        attn_state: AscendAttentionState = AscendAttentionState.DecodeOnly,
+        model: Optional[nn.Module] = None,
+    ):
+        if attn_state == AscendAttentionState.DecodeOnly:
+            attn_metadata = self.build(
+                common_prefix_len=0,
+                common_attn_metadata=common_attn_metadata,
+                model=model,
+            )
+        else:
+            raise NotImplementedError(
+                "Currently we only support building dummy metadata for DecodeOnly state"
+            )
+
+        attn_metadata.attn_state = attn_state
+        return attn_metadata
+
 
 class AscendSFAImpl(MLAAttentionImpl):
     """

vllm_ascend/worker/model_runner_v1.py

@@ -1894,7 +1894,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         )
 
         forward_context = get_forward_context()
-        if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL:
+        if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL \
+            and not self.use_sparse:
             # TODO: maybe_padded_num_tokens will be removed, use num_input_tokens instead
             if self.vllm_config.model_config.use_mla:
                 if self.pcp_size * self.dcp_size > 1:
@@ -2687,11 +2688,15 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                         [0] * dcp_world_size for _ in range(pcp_world_size)
                     ] for _ in range(num_tokens)]
                     long_seq_metadata.num_computed_tokens_of_pcp_dcp = num_computed_tokens_of_pcp_dcp
-                common_attn_metadata = AscendCommonAttentionMetadata(
+                if self.speculative_config:
-                    query_start_loc=torch.tensor(
+                    query_start_loc = torch.tensor(
                         [0] + self.actual_seq_lengths_q[:num_reqs],
                         device=self.device,
-                        dtype=torch.int32),
+                        dtype=torch.int32)
+                else:
+                    query_start_loc = self.query_start_loc[:num_reqs + 1]
+                common_attn_metadata = AscendCommonAttentionMetadata(
+                    query_start_loc=query_start_loc,
                     query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs +
                                                                  1],
                     seq_lens_cpu=self.seq_lens_cpu,
@@ -2737,7 +2742,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         forward_context = get_forward_context()
         assert forward_context is not None
         if forward_context.cudagraph_runtime_mode == CUDAGraphMode.FULL and \
-            not forward_context.capturing:
+            not forward_context.capturing and not self.use_sparse:
             if self.vllm_config.model_config.use_mla:
                 # FIXME: Try using `auto_dispatch_capture=True`
                 if self.pcp_size * self.dcp_size > 1: