[Feat] 310p supports PrefillCacheHit State (#6756)

### What this PR does / why we need it?
This PR extends the Ascend 310P attention backend to support the
`PrefillCacheHit` state. Previously, only `PrefillNoCache`,
`DecodeOnly`, and `ChunkedPrefill` were supported.
This PR handles this state by routing it to the existing
`forward_chunked_prefill_310` implementation, which is suitable for this
scenario.
The changes also include refactoring the main `forward_impl` dispatch
method for better clarity and updating unit tests to cover the new state
and ensure correctness.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Accuracy test when chunked prefill is disabled.
- vLLM version: v0.15.0
- vLLM main:
9562912cea

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

tests/ut/_310p/attention/test_attention_v1_310.py

@@ -78,7 +78,7 @@ class TestAscendAttentionBackendImpl310(TestBase):
     def test_forward_prefill_310(
         self, mock_get_forward_context, mock_npu_npu_flash_attention, mock_npu_reshape_and_cache
     ):
-        """Test forward pass in PrefillCacheHit state"""
+        """Test forward pass in PrefillNoCache state"""
         query = torch.randn(10, 8, 64)
         key = torch.randn(10, 8, 64)
         value = torch.randn(10, 8, 64)
@@ -98,7 +98,7 @@ class TestAscendAttentionBackendImpl310(TestBase):
 
         mock_get_forward_context.return_value = MagicMock(capturing=False)
         mock_npu_npu_flash_attention.return_value = torch.ones(10, 8, 64)
-        output = self.impl.forward_prefill_310(query, key, value, metadata, output)
+        output = self.impl.forward_impl(query, key, value, None, metadata, output)
 
         mock_npu_npu_flash_attention.assert_called_once()
 
@@ -107,10 +107,15 @@ class TestAscendAttentionBackendImpl310(TestBase):
     @patch("torch_npu._npu_paged_attention_splitfuse")
     @patch("vllm_ascend.attention.attention_v1.get_forward_context")
     def test_forward_chunked_prefill_310(
-        self, mock_get_forward_context, mock_npu_paged_attention_splitfuse, mock_npu_reshape_and_cache, mock_format_cast
+        self,
+        mock_get_forward_context,
+        mock_npu_paged_attention_splitfuse,
+        mock_npu_reshape_and_cache,
+        mock_format_cast,
     ):
-        """Test forward pass in PrefillCacheHit state"""
+        """Test forward pass in ChunkedPrefill state"""
         query = torch.randn(5, 8, 64)
+        key, value = None, None
         output = torch.empty_like(query)
         metadata = self.attn_metadata
         metadata.attn_state = AscendAttentionState.ChunkedPrefill
@@ -128,7 +133,42 @@ class TestAscendAttentionBackendImpl310(TestBase):
 
         mock_get_forward_context.return_value = MagicMock(capturing=False)
         mock_npu_paged_attention_splitfuse.return_value = torch.ones(5, 8, 64)
-        output = self.impl.forward_chunked_prefill_310(query, metadata, output)
+        output = self.impl.forward_impl(query, key, value, None, metadata, output)
+
+        mock_npu_paged_attention_splitfuse.assert_called_once()
+
+    @patch("torch_npu.npu_format_cast", return_value=torch.randn((1, 128, 16, 16), dtype=torch.float16))
+    @patch("torch_npu._npu_reshape_and_cache")
+    @patch("torch_npu._npu_paged_attention_splitfuse")
+    @patch("vllm_ascend.attention.attention_v1.get_forward_context")
+    def test_forward_prefill_cache_hit_310(
+        self,
+        mock_get_forward_context,
+        mock_npu_paged_attention_splitfuse,
+        mock_npu_reshape_and_cache,
+        mock_format_cast,
+    ):
+        """Test forward pass in PrefillCacheHit state"""
+        query = torch.randn(5, 8, 64)
+        key, value = None, None
+        output = torch.empty_like(query)
+        metadata = self.attn_metadata
+        metadata.attn_state = AscendAttentionState.PrefillCacheHit
+        metadata.attn_mask = torch.randn(1, 128, 16, 16)
+        metadata.query_lens = torch.tensor([5])
+        metadata.seq_lens = torch.tensor([1, 4])
+        metadata.query_start_loc = torch.tensor([0, 1, 5])
+        metadata.actual_seq_lengths_q = [5]
+        metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
+        metadata.num_actual_tokens = 10
+        metadata.num_decode_tokens = 0
+        metadata.num_decodes = 0
+        metadata.num_prefills = 10
+        metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
+
+        mock_get_forward_context.return_value = MagicMock(capturing=False)
+        mock_npu_paged_attention_splitfuse.return_value = torch.ones(5, 8, 64)
+        output = self.impl.forward_impl(query, key, value, None, metadata, output)
 
         mock_npu_paged_attention_splitfuse.assert_called_once()
 
@@ -141,6 +181,7 @@ class TestAscendAttentionBackendImpl310(TestBase):
     ):
         """Test forward pass in DecodeOnly state"""
         query = torch.randn(4, 8 * 64)
+        key, value = None, None
         output = torch.empty_like(query)
 
         metadata = self.attn_metadata
@@ -155,6 +196,15 @@ class TestAscendAttentionBackendImpl310(TestBase):
 
         mock_get_forward_context.return_value = MagicMock(capturing=False)
 
-        output = self.impl.forward_paged_attention(query, metadata, output)
+        output = self.impl.forward_impl(query, key, value, None, metadata, output)
 
         mock_paged_attention.assert_called_once()
+
+    def test_forward_mtp_310(self):
+        query = torch.randn(4, 8 * 64)
+        key, value = None, None
+        output = torch.empty_like(query)
+        metadata = self.attn_metadata
+        metadata.attn_state = AscendAttentionState.SpecDecoding
+        with self.assertRaises(NotImplementedError):
+            output = self.impl.forward_impl(query, key, value, None, metadata, output)

vllm_ascend/_310p/attention/attention_v1.py

@@ -198,6 +198,8 @@ class AscendAttentionBackendImpl310(AscendAttentionBackendImpl):
             out=output,
         )
 
+        return output
+
     def forward_impl(self, query, key, value, kv_cache, attn_metadata, output):
         """
         Main dispatch method for attention operations.
@@ -218,22 +220,19 @@ class AscendAttentionBackendImpl310(AscendAttentionBackendImpl):
             NotImplementedError: If the attention state is not supported on 310P.
         """
         state = attn_metadata.attn_state
-
+        # Condition for PrefillNoCache: No previous tokens have been processed yet
-        if state == AscendAttentionState.DecodeOnly:
-            return self.forward_paged_attention(query, attn_metadata, output)
-
         if state == AscendAttentionState.PrefillNoCache:
-            out = self.forward_prefill_310(query, key, value, attn_metadata, output)
+            output = self.forward_prefill_310(query, key, value, attn_metadata, output)
-            return out
+        # Condition for DecodeOnly: Pure decoding phase where each request generates one token
-
+        elif state == AscendAttentionState.DecodeOnly:
-        if state == AscendAttentionState.ChunkedPrefill:
+            output = self.forward_paged_attention(query, attn_metadata, output)
-            self.forward_chunked_prefill_310(query, attn_metadata, output)
+        # Condition for ChunkedPrefill:
-            return output
+        # 1. During speculative decoding scenarios (except mtp)
-
+        # 2. Processing large prefill requests in chunks
-        raise NotImplementedError(
+        # Condition for PrefillCacheHit: Indicates prefill with some cached tokens already processed
-            f"{self.__class__.__name__}.forward_impl: 310P only supports "
+        elif state in [AscendAttentionState.ChunkedPrefill, AscendAttentionState.PrefillCacheHit]:
-            f"{AscendAttentionState.DecodeOnly.name}, "
+            output = self.forward_chunked_prefill_310(query, attn_metadata, output)
-            f"{AscendAttentionState.PrefillNoCache.name}, "
+        # Condition for SpecDecoding: Specified for mtp, which is not supported yet.
-            f"{AscendAttentionState.ChunkedPrefill.name}, "
+        else:
-            f"got {state!r}."
+            raise NotImplementedError(f"AscendAttentionState: {state} is not supported for 310P currently.")
-        )
+        return output

vllm_ascend/_310p/model_runner_310p.py

@@ -17,9 +17,11 @@
 
 from __future__ import annotations
 
+import numpy as np
 import torch
 import torch_npu
 from vllm.logger import logger
+from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheConfig, MambaSpec
 
 from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ
@@ -185,3 +187,97 @@ class NPUModelRunner310(NPUModelRunner):
                     raise ValueError("Unknown KV cache spec type.")
 
         return kv_caches
+
+    # Override this function because of tensor.copy_(other) accuracy issue.
+    # TODO: This override will be removed after tensor.copy_(other) accuracy issue is resolved.
+    def _prepare_input_ids(
+        self,
+        scheduler_output: SchedulerOutput,
+        total_num_scheduled_tokens: int,
+        cu_num_tokens: np.ndarray,
+    ) -> None:
+        """Prepare the input IDs for the current batch.
+
+        Carefully handles the `prev_sampled_token_ids` which can be cached
+        from the previous engine iteration, in which case those tokens on the
+        GPU need to be copied into the corresponding slots into input_ids."""
+
+        if self.input_batch.prev_sampled_token_ids is None:
+            # Normal scheduling case
+            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
+            if self.enable_prompt_embeds:
+                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
+                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
+            return
+
+        # Async scheduling case, where some decode requests from the previous
+        # iteration won't have entries in input_ids_cpu and need to be copied
+        # on the NPU from prev_sampled_token_ids.
+        prev_req_id_to_index = self.input_batch.prev_req_id_to_index
+        assert prev_req_id_to_index is not None
+        sample_flattened_indices: list[int] = []
+        spec_flattened_indices: list[int] = []
+        prev_common_req_indices: list[int] = []
+        prev_draft_token_indices: list[int] = []
+        indices_match = True
+        max_flattened_index = -1
+        total_num_spec_tokens = 0
+        scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens
+
+        for req_id, cur_index in self.input_batch.req_id_to_index.items():
+            if (prev_index := prev_req_id_to_index.get(req_id)) is not None:
+                prev_common_req_indices.append(prev_index)
+                draft_len = len(scheduled_spec_tokens.get(req_id, ()))
+                total_num_spec_tokens += draft_len
+                flattened_index = cu_num_tokens[cur_index].item() - 1
+                sample_flattened_indices.append(flattened_index - draft_len)
+                spec_flattened_indices.extend(range(flattened_index - draft_len + 1, flattened_index + 1))
+                start = prev_index * self.num_spec_tokens
+                prev_draft_token_indices.extend(range(start, start + draft_len))
+                indices_match &= prev_index == flattened_index
+                max_flattened_index = max(max_flattened_index, flattened_index)
+        num_commmon_tokens = len(sample_flattened_indices)
+        total_without_spec = total_num_scheduled_tokens - total_num_spec_tokens
+        if num_commmon_tokens < total_without_spec:
+            self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
+            if self.enable_prompt_embeds:
+                self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
+                self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
+        if num_commmon_tokens == 0:
+            return
+        if indices_match and max_flattened_index == (num_commmon_tokens - 1):
+            # NOTE: Override the copy_ function here
+            indices = torch.arange(num_commmon_tokens, device=self.input_ids.gpu.device)
+            source = self.input_batch.prev_sampled_token_ids[:num_commmon_tokens, 0]
+            self.input_ids.gpu.index_copy_(0, indices, source)
+            if self.enable_prompt_embeds:
+                self.is_token_ids.gpu[:num_commmon_tokens] = True
+            return
+        # Upload the index tensors asynchronously so the scatter can be non-blocking.
+        sampled_tokens_index_tensor = torch.tensor(
+            sample_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
+        ).to(self.device, non_blocking=True)
+        prev_common_req_indices_tensor = torch.tensor(
+            prev_common_req_indices, dtype=torch.int64, pin_memory=self.pin_memory
+        ).to(self.device, non_blocking=True)
+        self.input_ids.gpu.scatter_(
+            dim=0,
+            index=sampled_tokens_index_tensor,
+            src=self.input_batch.prev_sampled_token_ids[prev_common_req_indices_tensor, 0],
+        )
+        # Scatter the draft tokens after the sampled tokens are scattered.
+        if self._draft_token_ids is None or not spec_flattened_indices:
+            return
+        assert isinstance(self._draft_token_ids, torch.Tensor)
+        draft_tokens_index_tensor = torch.tensor(
+            spec_flattened_indices, dtype=torch.int64, pin_memory=self.pin_memory
+        ).to(self.device, non_blocking=True)
+        prev_draft_token_indices_tensor = torch.tensor(
+            prev_draft_token_indices, dtype=torch.int64, pin_memory=self.pin_memory
+        ).to(self.device, non_blocking=True)
+        draft_token_ids = self._draft_token_ids.to(dtype=torch.int32)
+        self.input_ids.gpu.scatter_(
+            dim=0,
+            index=draft_tokens_index_tensor,
+            src=draft_token_ids.flatten()[prev_draft_token_indices_tensor],
+        )