[0.11.0][BugFix] Improve the performance of prefixcache features (#4021)

### What this PR does / why we need it?
cherry-pick from https://github.com/vllm-project/vllm-ascend/pull/4022

The code bug caused an empty bubble. When the npu_paged_cache_load
operator was called, it forcibly transferred seq_len2 to the device,
which triggered synchronization and interrupted the CPU operator's
launch stream.


---------

Signed-off-by: underfituu <hzhucong@163.com>

tests/ut/attention/test_mla_v1.py

@@ -82,7 +82,8 @@ class TestAscendMLAPrefillMetadata(TestBase):
             seq_tot=seq_tot,
             max_seq_lens=max_seq_lens,
             workspace=workspace,
-            chunk_seq_lens=chunk_seq_lens)
+            chunk_seq_lens=chunk_seq_lens,
+            chunk_seq_lens_npu=chunk_seq_lens)
 
         metadata = AscendMLAPrefillMetadata(
             attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool),
@@ -103,6 +104,8 @@ class TestAscendMLAPrefillMetadata(TestBase):
         self.assertEqual(metadata.chunked_context.max_seq_lens, max_seq_lens)
         self.assertIs(metadata.chunked_context.workspace, workspace)
         self.assertIs(metadata.chunked_context.chunk_seq_lens, chunk_seq_lens)
+        self.assertIs(metadata.chunked_context.chunk_seq_lens_npu,
+                      chunk_seq_lens)
 
 
 class TestAscendMLADecodeMetadata(TestBase):
@@ -428,6 +431,7 @@ class TestAscendMLAImpl(TestBase):
         chunk_ctx = MagicMock()
         chunk_ctx.seq_tot = [8]
         chunk_ctx.chunk_seq_lens = [torch.tensor([8])]
+        chunk_ctx.chunk_seq_lens_npu = [torch.tensor([8])]
         chunk_ctx.starts = [torch.tensor([0])]
 
         prefill_meta = MagicMock()

tests/ut/torchair/test_torchair_mla.py

@@ -86,7 +86,8 @@ class TestAscendMLATorchairPrefillMetadata(TestBase):
             seq_tot=seq_tot,
             max_seq_lens=max_seq_lens,
             workspace=workspace,
-            chunk_seq_lens=chunk_seq_lens)
+            chunk_seq_lens=chunk_seq_lens,
+            chunk_seq_lens_npu=chunk_seq_lens)
 
         metadata = AscendMLATorchairPrefillMetadata(
             attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool),
@@ -107,6 +108,8 @@ class TestAscendMLATorchairPrefillMetadata(TestBase):
         self.assertEqual(metadata.chunked_context.max_seq_lens, max_seq_lens)
         self.assertIs(metadata.chunked_context.workspace, workspace)
         self.assertIs(metadata.chunked_context.chunk_seq_lens, chunk_seq_lens)
+        self.assertIs(metadata.chunked_context.chunk_seq_lens_npu,
+                      chunk_seq_lens)
 
 
 class TestAscendMLATorchairDecodeMetadata(TestBase):
@@ -661,6 +664,7 @@ class TestAscendMLATorchairImpl(TestBase):
         chunk_ctx = MagicMock()
         chunk_ctx.seq_tot = [8]
         chunk_ctx.chunk_seq_lens = [torch.tensor([8])]
+        chunk_ctx.chunk_seq_lens_npu = [torch.tensor([8])]
         chunk_ctx.starts = [torch.tensor([0])]
 
         prefill_meta = MagicMock()

vllm_ascend/attention/mla_v1.py

@@ -80,6 +80,7 @@ class AscendMLAPrefillMetadata:
         max_seq_lens: list[int]
         workspace: torch.Tensor
         chunk_seq_lens: torch.Tensor
+        chunk_seq_lens_npu: torch.Tensor
 
     attn_mask: torch.Tensor
     query_lens: torch.Tensor
@@ -371,6 +372,7 @@ class AscendMLAMetadataBuilder:
                     seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
                     max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                     chunk_seq_lens=chunk_seq_lens,
+                    chunk_seq_lens_npu=chunk_seq_lens.npu(),
                     workspace=self.chunked_prefill_workspace,
                 )
             prefill_input_positions = input_positions[tokens_start:]
@@ -766,7 +768,8 @@ class AscendMLAImpl(MLAAttentionImpl):
 
         iters = len(prefill_metadata.chunked_context.seq_tot)
 
-        seq_len1 = torch.tensor(prefill_metadata.query_lens, dtype=torch.int32)
+        current_seq_len = torch.tensor(prefill_metadata.query_lens,
+                                       dtype=torch.int32)
         cache_kv_c = kv_c_and_k_pe_cache[0]
         cache_k_pe = kv_c_and_k_pe_cache[1]
         num_heads = cache_k_pe.size(2)
@@ -774,8 +777,11 @@ class AscendMLAImpl(MLAAttentionImpl):
         for i in range(iters):
             toks = prefill_metadata.chunked_context.seq_tot[i]
 
-            seq_len2 = prefill_metadata.chunked_context.chunk_seq_lens[i]
-            seq_len = torch.stack([seq_len1, seq_len2])
+            context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[
+                i]
+            context_seq_len_npu = prefill_metadata.chunked_context.chunk_seq_lens_npu[
+                i]
+            seq_len = torch.stack([current_seq_len, context_seq_len])
             kv_c_normed = torch.empty(toks,
                                       num_heads,
                                       latent_kv_dim,
@@ -791,7 +797,7 @@ class AscendMLAImpl(MLAAttentionImpl):
                 cache_kv_c,
                 cache_k_pe,
                 prefill_metadata.block_table,
-                seq_len2.to(q_nope.device),
+                context_seq_len_npu,
                 seq_starts=prefill_metadata.chunked_context.starts[i],
                 key=kv_c_normed,
                 value=k_pe,

vllm_ascend/torchair/torchair_mla.py

@@ -72,6 +72,7 @@ class AscendMLATorchairPrefillMetadata:
         max_seq_lens: list[int]
         workspace: torch.Tensor
         chunk_seq_lens: torch.Tensor
+        chunk_seq_lens_npu: torch.Tensor
 
     attn_mask: torch.Tensor
     query_lens: torch.Tensor
@@ -462,6 +463,7 @@ class AscendMLATorchairMetadataBuilder:
                     seq_tot=chunk_seq_lens.sum(dim=1).tolist(),
                     max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                     chunk_seq_lens=chunk_seq_lens,
+                    chunk_seq_lens_npu=chunk_seq_lens.npu(),
                     workspace=self.chunked_prefill_workspace,
                 )
             prefill_input_positions = input_positions[tokens_start:]
@@ -777,7 +779,8 @@ class AscendMLATorchairImpl(MLAAttentionImpl):
         q_pe = query[..., self.qk_nope_head_dim:]
         q_nope = query[..., :self.qk_nope_head_dim]
 
-        seq_len1 = torch.tensor(prefill_metadata.query_lens, dtype=torch.int32)
+        current_seq_len = torch.tensor(prefill_metadata.query_lens,
+                                       dtype=torch.int32)
         cache_kv_c = kv_c_and_k_pe_cache[0]
         cache_k_pe = kv_c_and_k_pe_cache[1]
         num_heads = cache_k_pe.size(2)
@@ -785,8 +788,11 @@ class AscendMLATorchairImpl(MLAAttentionImpl):
         for i in range(iters):
             toks = prefill_metadata.chunked_context.seq_tot[i]
 
-            seq_len2 = prefill_metadata.chunked_context.chunk_seq_lens[i]
-            seq_len = torch.stack([seq_len1, seq_len2])
+            context_seq_len = prefill_metadata.chunked_context.chunk_seq_lens[
+                i]
+            context_seq_len_npu = prefill_metadata.chunked_context.chunk_seq_lens_npu[
+                i]
+            seq_len = torch.stack([current_seq_len, context_seq_len])
             kv_c_normed = torch.empty(toks,
                                       num_heads,
                                       latent_kv_dim,
@@ -802,7 +808,7 @@ class AscendMLATorchairImpl(MLAAttentionImpl):
                 cache_kv_c,
                 cache_k_pe,
                 prefill_metadata.block_table,
-                seq_len2.to(query.device),
+                context_seq_len_npu,
                 seq_starts=prefill_metadata.chunked_context.starts[i],
                 key=kv_c_normed,
                 value=k_pe,