update mla interface

vllm/attention/ops/flashmla.py

@@ -77,9 +77,9 @@ def get_mla_metadata(
     - num_splits: (batch_size + 1), dtype torch.int32.
     """
     if current_platform.is_rocm():
-        return flash_mla_cuda.get_mla_metadata(cache_seqlens,
-                                num_q_tokens_per_head_k,
-                                num_heads_k)
+        return flash_mla_cuda.get_mla_metadata(
+            cache_seqlens, num_q_tokens_per_head_k, num_heads_k, num_heads_q,
+            is_fp8_kvcache, topk)
     else:
         return torch.ops._flashmla_C.get_mla_decoding_metadata(
             cache_seqlens, num_q_tokens_per_head_k, num_heads_k, num_heads_q,
@@ -160,8 +160,9 @@ def flash_mla_with_kvcache(
     else:
         if current_platform.is_rocm():
             out, softmax_lse = flash_mla_cuda.fwd_kvcache_mla(
-                q, k_cache, None, head_dim_v, cache_seqlens, block_table, softmax_scale,
-                causal, tile_scheduler_metadata, num_splits)
+                q, k_cache, block_table, cache_seqlens, head_dim_v, tile_scheduler_metadata,
+                num_splits, softmax_scale, causal,  is_fp8_kvcache,
+                indices)
         else:
             out, softmax_lse = torch.ops._flashmla_C.fwd_kvcache_mla(
                 q, k_cache, head_dim_v, cache_seqlens, block_table, softmax_scale,
@@ -196,8 +197,12 @@ def flash_mla_sparse_prefill(
     - max_logits:  [s_q, h_q], float
     - lse: [s_q, h_q], float, 2-based log-sum-exp
     """
-    results = torch.ops._flashmla_C.sparse_prefill_fwd(q, kv, indices,
-                                                       sm_scale, d_v)
+    if current_platform.is_rocm():
+        return flash_mla_cuda.sparse_prefill_fwd(q, kv, indices,
+                                                        sm_scale, d_v)
+    else:
+        results = torch.ops._flashmla_C.sparse_prefill_fwd(q, kv, indices,
+                                                        sm_scale, d_v)
     return results