move checks out of `unified_kv_cache_update` custom op (#33943)

Signed-off-by: Rohan138 <rohanpotdar138@gmail.com>

vllm/model_executor/layers/attention/attention.py

@@ -422,9 +422,15 @@ class Attention(nn.Module, AttentionLayerBase):
                 key = key.view(-1, self.num_kv_heads, self.head_size)
             if value is not None:
                 value = value.view(-1, self.num_kv_heads, self.head_size_v)
-            if self.use_direct_call:
             kv_cache_dummy_dep = None
-                if not self.attn_backend.forward_includes_kv_cache_update:
+            if self.use_direct_call:
+                # Skip this if sharing KV cache with an earlier attention layer.
+                if (
+                    not self.attn_backend.forward_includes_kv_cache_update
+                    and self.kv_sharing_target_layer_name is None
+                    and key is not None
+                    and value is not None
+                ):
                     kv_cache_dummy_dep = unified_kv_cache_update(
                         key, value, self.layer_name
                     )
@@ -437,10 +443,12 @@ class Attention(nn.Module, AttentionLayerBase):
                     kv_cache_dummy_dep=kv_cache_dummy_dep,
                 )
             else:
-                kv_cache_dummy_dep = None
-                if not self.attn_backend.forward_includes_kv_cache_update and (
-                    # torch can only dispatch custom op if a tensor is passed
-                    key is not None or value is not None
+                # Skip this if sharing KV cache with an earlier attention layer.
+                if (
+                    not self.attn_backend.forward_includes_kv_cache_update
+                    and self.kv_sharing_target_layer_name is None
+                    and key is not None
+                    and value is not None
                 ):
                     kv_cache_dummy_dep = torch.ops.vllm.unified_kv_cache_update(
                         key, value, self.layer_name

vllm/model_executor/layers/attention/cross_attention.py

@@ -136,6 +136,9 @@ def create_cross_attention_backend(
             if (
                 not underlying_attn_backend.forward_includes_kv_cache_update
                 and attn_metadata is not None
+                and layer.kv_sharing_target_layer_name is None
+                and key is not None
+                and value is not None
             ):
                 self.do_kv_cache_update(
                     layer, key, value, kv_cache, attn_metadata.slot_mapping

vllm/model_executor/models/whisper_causal.py

@@ -172,6 +172,9 @@ def create_whisper_attention_backend_with_block_pooling(
             if (
                 not underlying_attn_backend.forward_includes_kv_cache_update
                 and attn_metadata is not None
+                and layer.kv_sharing_target_layer_name is None
+                and key is not None
+                and value is not None
             ):
                 self.do_kv_cache_update(
                     layer, key, value, kv_cache, attn_metadata.slot_mapping

vllm/v1/attention/backends/flash_attn.py

@@ -771,16 +771,6 @@ class FlashAttentionImpl(AttentionImpl):
             # we use direct Q, K, V tensors without caching
             return
 
-        # key and value may be None in the case of cross attention. They are
-        # calculated once based on the output from the encoder and then cached
-        # in KV cache.
-        if (
-            self.kv_sharing_target_layer_name is not None
-            or key is None
-            or value is None
-        ):
-            return
-
         key_cache, value_cache = kv_cache.unbind(0)
 
         # Reshape the input keys and values and store them in the cache.

vllm/v1/attention/backends/rocm_aiter_unified_attn.py

@@ -196,16 +196,7 @@ class RocmAiterUnifiedAttentionImpl(RocmAttentionImpl):
     ):
         key_cache, value_cache = kv_cache.unbind(0)
 
-        # key and value may be None in the case of cross attention. They are
-        # calculated once based on the output from the encoder and then cached
-        # in KV cache.
-        if (
-            self.kv_sharing_target_layer_name is None
-            and key is not None
-            and value is not None
-        ):
         # Reshape the input keys and values and store them in the cache.
-            # Skip this if sharing KV cache with an earlier attention layer.
         ops.reshape_and_cache_flash(
             key,
             value,

vllm/v1/attention/backends/rocm_attn.py

@@ -383,17 +383,7 @@ class RocmAttentionImpl(AttentionImpl):
             kv_cache, self.num_kv_heads, self.head_size
         )
 
-        # key and value may be None in the case of cross attention. They are
-        # calculated once based on the output from the encoder and then cached
-        # in KV cache.
-        if (
-            self.kv_sharing_target_layer_name is None
-            and key is not None
-            and value is not None
-        ):
         # Reshape the input keys and values and store them in the cache.
-            # Skip this if sharing KV cache with an earlier attention layer.
-
         # Get the actual block_size from value_cache
         # value_cache shape: [num_blocks, num_heads, head_size, block_size]
         block_size = value_cache.shape[3]

vllm/v1/attention/backends/triton_attn.py

@@ -579,13 +579,7 @@ class TritonAttentionImpl(AttentionImpl):
         # For decoder and cross-attention, use KV cache as before
         key_cache, value_cache = kv_cache.unbind(1)
 
-        if (
-            self.kv_sharing_target_layer_name is None
-            and key is not None
-            and value is not None
-        ):
         # Reshape the input keys and values and store them in the cache.
-            # Skip this if sharing KV cache with an earlier attention layer.
         if self.kv_cache_dtype.startswith("fp8"):
             key_cache = key_cache.view(self.fp8_dtype)
             value_cache = value_cache.view(self.fp8_dtype)