diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index eed3cba9a2ca7..eca83b6d2ee45 100755
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -405,13 +405,6 @@ class FlashAttentionImpl(AttentionImpl):
 
         FlashAttentionBackend.validate_head_size(head_size)
 
-        if attn_type not in [
-                AttentionType.DECODER, AttentionType.ENCODER_ONLY
-        ]:
-            raise NotImplementedError("Encoder/decoder cross-attention "
-                                      "is not implemented for "
-                                      "FlashAttentionImpl")
-
         self.attn_type = attn_type
         self.vllm_flash_attn_version = get_flash_attn_version()
         if is_quantized_kv_cache(self.kv_cache_dtype) \
@@ -477,7 +470,7 @@ class FlashAttentionImpl(AttentionImpl):
         num_actual_tokens = attn_metadata.num_actual_tokens
 
         # Handle encoder attention differently - no KV cache needed
-        if attn_type in (AttentionType.ENCODER_ONLY, ):
+        if attn_type in (AttentionType.ENCODER_ONLY, AttentionType.ENCODER):
             # For encoder attention,
             # we use direct Q, K, V tensors without caching
             return self._forward_encoder_attention(query[:num_actual_tokens],
@@ -489,7 +482,11 @@ class FlashAttentionImpl(AttentionImpl):
         # For decoder and cross-attention, use KV cache as before
         key_cache, value_cache = kv_cache.unbind(0)
 
-        if self.kv_sharing_target_layer_name is None:
+        # 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.
             # NOTE(woosuk): Here, key and value are padded while slot_mapping is
@@ -528,7 +525,7 @@ class FlashAttentionImpl(AttentionImpl):
             block_table = attn_metadata.block_table
             scheduler_metadata = attn_metadata.scheduler_metadata
 
-            descale_shape = (cu_seqlens_q.shape[0] - 1, key.shape[1])
+            descale_shape = (cu_seqlens_q.shape[0] - 1, self.num_kv_heads)
 
             flash_attn_varlen_func(
                 q=query[:num_actual_tokens],