[V1][Minor] Move cascade attn logic outside _prepare_inputs (#12943)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

vllm/v1/worker/gpu_model_runner.py

@@ -476,67 +476,11 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             self.device, non_blocking=True).long()
 
         # Prepare for cascade attention if needed.
-        common_prefix_len = (scheduler_output.num_common_prefix_blocks *
+        common_prefix_len = self._compute_cascade_attn_prefix_len(
-                             self.block_size)
+            num_scheduled_tokens,
-        if common_prefix_len == 0:
+            scheduler_output.num_common_prefix_blocks,
-            # Common case.
+        )
-            use_cascade = False
+        use_cascade = common_prefix_len > 0
-        else:
-            # NOTE(woosuk): Cascade attention uses two attention kernels: one
-            # for the common prefix and the other for the rest. For the first
-            # kernel, we concatenate all the query tokens (possibly from
-            # different requests) and treat them as if they are from the same
-            # request. Then, we use bi-directional attention to process the
-            # common prefix in the KV cache. Importantly, this means that the
-            # first kernel does not do any masking.
-
-            # Consider the following example:
-            # Request 1's input query: [D, E, X]
-            # Request 1's kv cache: [A, B, C, D, E, X]
-            # Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
-            # Request 2's input query: [E, Y]
-            # Request 2's kv cache: [A, B, C, D, E, Y]
-            # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])
-
-            # If we use [A, B, C, D, E] as the common prefix, then the
-            # first kernel will compute the bi-directional attention between
-            # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
-            # However, this is wrong because D in Request 1 should not attend to
-            # E in the common prefix (i.e., we need masking).
-            # To avoid this, [A, B, C, D] should be the common prefix.
-            # That is, the common prefix should be capped by the minimum
-            # num_computed_tokens among the requests, and plus one to include
-            # the first token of the query.
-
-            # In practice, we use [A, B, C] as the common prefix, instead of
-            # [A, B, C, D] (i.e., the common prefix is capped by the minimum
-            # num_computed_tokens, without plus one).
-            # This is because of an implementation detail: We want to always
-            # use two kernels for cascade attention. Let's imagine:
-            # Request 3's input query: [D]
-            # Request 3's kv cache: [A, B, C, D]
-            # Request 3's num_computed_tokens: 4 (i.e., [A, B, C, D])
-            # If we use [A, B, C, D] as the common prefix for Request 1-3,
-            # then Request 3 will be processed only by the first kernel,
-            # and the second kernel will get an empty input. While this is not
-            # a fundamental problem, our current implementation does not support
-            # this case.
-            common_prefix_len = min(
-                common_prefix_len,
-                self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
-            # common_prefix_len should be a multiple of the block size.
-            common_prefix_len = (common_prefix_len // self.block_size *
-                                 self.block_size)
-            use_cascade = FlashAttentionBackend.use_cascade_attention(
-                common_prefix_len=common_prefix_len,
-                query_lens=num_scheduled_tokens,
-                num_query_heads=self.num_query_heads,
-                num_kv_heads=self.num_kv_heads,
-                use_alibi=False,  # FIXME
-                use_sliding_window=self.sliding_window is not None,
-                num_sms=self.num_sms,
-            )
-
         if use_cascade:
             # TODO: Optimize.
             cu_prefix_query_lens = torch.tensor(
@@ -581,6 +525,90 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         logits_indices = query_start_loc[1:] - 1
         return attn_metadata, logits_indices
 
+    def _compute_cascade_attn_prefix_len(
+        self,
+        num_scheduled_tokens: np.ndarray,
+        num_common_prefix_blocks: int,
+    ) -> int:
+        """Compute the length of the common prefix for cascade attention.
+
+        NOTE(woosuk): The common prefix length returned by this function
+        represents the length used specifically for cascade attention, not the
+        actual number of tokens shared between requests. When cascade attention
+        is disabled (use_cascade=False), this function returns 0 even if
+        requests share common tokens. Additionally, the common prefix length is
+        truncated to a multiple of the block size and may be further truncated
+        due to implementation details explained below.
+
+        Args:
+            num_scheduled_tokens: Number of tokens scheduled per request.
+            num_common_prefix_blocks: Number of shared KV cache blocks.
+
+        Returns:
+            int: Length of common prefix in tokens.
+        """
+        common_prefix_len = num_common_prefix_blocks * self.block_size
+        if common_prefix_len == 0:
+            # Common case.
+            return 0
+
+        # NOTE(woosuk): Cascade attention uses two attention kernels: one
+        # for the common prefix and the other for the rest. For the first
+        # kernel, we concatenate all the query tokens (possibly from
+        # different requests) and treat them as if they are from the same
+        # request. Then, we use bi-directional attention to process the
+        # common prefix in the KV cache. Importantly, this means that the
+        # first kernel does not do any masking.
+
+        # Consider the following example:
+        # Request 1's input query: [D, E, X]
+        # Request 1's kv cache: [A, B, C, D, E, X]
+        # Request 1's num_computed_tokens: 3 (i.e., [A, B, C])
+        # Request 2's input query: [E, Y]
+        # Request 2's kv cache: [A, B, C, D, E, Y]
+        # Request 2's num_computed_tokens: 4 (i.e., [A, B, C, D])
+
+        # If we use [A, B, C, D, E] as the common prefix, then the
+        # first kernel will compute the bi-directional attention between
+        # input query [D, E, X, E, Y] and common prefix [A, B, C, D, E].
+        # However, this is wrong because D in Request 1 should not attend to
+        # E in the common prefix (i.e., we need masking).
+        # To avoid this, [A, B, C, D] should be the common prefix.
+        # That is, the common prefix should be capped by the minimum
+        # num_computed_tokens among the requests, and plus one to include
+        # the first token of the query.
+
+        # In practice, we use [A, B, C] as the common prefix, instead of
+        # [A, B, C, D] (i.e., the common prefix is capped by the minimum
+        # num_computed_tokens, without plus one).
+        # This is because of an implementation detail: We want to always
+        # use two kernels for cascade attention. Let's imagine:
+        # Request 3's input query: [D]
+        # Request 3's kv cache: [A, B, C, D]
+        # Request 3's num_computed_tokens: 4 (i.e., [A, B, C, D])
+        # If we use [A, B, C, D] as the common prefix for Request 1-3,
+        # then Request 3 will be processed only by the first kernel,
+        # and the second kernel will get an empty input. While this is not
+        # a fundamental problem, our current implementation does not support
+        # this case.
+        num_reqs = len(num_scheduled_tokens)
+        common_prefix_len = min(
+            common_prefix_len,
+            self.input_batch.num_computed_tokens_cpu[:num_reqs].min())
+        # common_prefix_len should be a multiple of the block size.
+        common_prefix_len = (common_prefix_len // self.block_size *
+                             self.block_size)
+        use_cascade = FlashAttentionBackend.use_cascade_attention(
+            common_prefix_len=common_prefix_len,
+            query_lens=num_scheduled_tokens,
+            num_query_heads=self.num_query_heads,
+            num_kv_heads=self.num_kv_heads,
+            use_alibi=False,  # FIXME
+            use_sliding_window=self.sliding_window is not None,
+            num_sms=self.num_sms,
+        )
+        return common_prefix_len if use_cascade else 0
+
     def _calc_mrope_positions(self, scheduler_output: "SchedulerOutput"):
         mrope_pos_ptr = 0
         num_reqs = self.input_batch.num_reqs