[Feature] Support NPUGraph for DeepSeek on Ascend NPU (#9355)

Co-authored-by: Even Zhou <even.y.zhou@outlook.com>

python/sglang/srt/disaggregation/ascend/conn.py

@@ -1,6 +1,12 @@
+import concurrent.futures
 import logging
+from typing import List, Tuple
+
+import numpy as np
+import numpy.typing as npt
 
 from sglang.srt.disaggregation.ascend.transfer_engine import AscendTransferEngine
+from sglang.srt.disaggregation.common.utils import group_concurrent_contiguous
 from sglang.srt.disaggregation.mooncake.conn import (
     MooncakeKVBootstrapServer,
     MooncakeKVManager,
@@ -29,6 +35,75 @@ class AscendKVManager(MooncakeKVManager):
             self.kv_args.aux_data_ptrs, self.kv_args.aux_data_lens
         )
 
+    def send_kvcache(
+        self,
+        mooncake_session_id: str,
+        prefill_kv_indices: npt.NDArray[np.int32],
+        dst_kv_ptrs: list[int],
+        dst_kv_indices: npt.NDArray[np.int32],
+        executor: concurrent.futures.ThreadPoolExecutor,
+    ):
+        # Group by indices
+        prefill_kv_blocks, dst_kv_blocks = group_concurrent_contiguous(
+            prefill_kv_indices, dst_kv_indices
+        )
+
+        num_layers = len(self.kv_args.kv_data_ptrs)
+        layers_params = [
+            (
+                self.kv_args.kv_data_ptrs[layer_id],
+                dst_kv_ptrs[layer_id],
+                self.kv_args.kv_item_lens[layer_id],
+            )
+            for layer_id in range(num_layers)
+        ]
+
+        def set_transfer_blocks(
+            src_ptr: int, dst_ptr: int, item_len: int
+        ) -> List[Tuple[int, int, int]]:
+            transfer_blocks = []
+            for prefill_index, decode_index in zip(prefill_kv_blocks, dst_kv_blocks):
+                src_addr = src_ptr + int(prefill_index[0]) * item_len
+                dst_addr = dst_ptr + int(decode_index[0]) * item_len
+                length = item_len * len(prefill_index)
+                transfer_blocks.append((src_addr, dst_addr, length))
+            return transfer_blocks
+
+        # Worker function for processing a single layer
+        def process_layer(src_ptr: int, dst_ptr: int, item_len: int) -> int:
+            transfer_blocks = set_transfer_blocks(src_ptr, dst_ptr, item_len)
+            return self._transfer_data(mooncake_session_id, transfer_blocks)
+
+        # Worker function for processing all layers in a batch
+        def process_layers(layers_params: List[Tuple[int, int, int]]) -> int:
+            transfer_blocks = []
+            for src_ptr, dst_ptr, item_len in layers_params:
+                transfer_blocks.extend(set_transfer_blocks(src_ptr, dst_ptr, item_len))
+            return self._transfer_data(mooncake_session_id, transfer_blocks)
+
+        if self.enable_custom_mem_pool:
+            futures = [
+                executor.submit(
+                    process_layer,
+                    src_ptr,
+                    dst_ptr,
+                    item_len,
+                )
+                for (src_ptr, dst_ptr, item_len) in layers_params
+            ]
+            for future in concurrent.futures.as_completed(futures):
+                status = future.result()
+                if status != 0:
+                    for f in futures:
+                        f.cancel()
+                    return status
+        else:
+            # Combining all layers' params in one batch transfer is more efficient
+            # compared to using multiple threads
+            return process_layers(layers_params)
+
+        return 0
+
 
 class AscendKVSender(MooncakeKVSender):
     pass