[V1][P/D] Local attention optimization for NIXL (#18170)

Signed-off-by: mgoin <mgoin64@gmail.com>

Author: mgoin

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -96,7 +96,8 @@ def __init__(self, vllm_config: VllmConfig, role: KVConnectorRole):
             self.connector_worker: Optional[NixlConnectorWorker] = None
         elif role == KVConnectorRole.WORKER:
             self.connector_scheduler = None
-            self.connector_worker = NixlConnectorWorker(str(self.engine_id))
+            self.connector_worker = NixlConnectorWorker(
+                vllm_config, str(self.engine_id))
 
     ############################################################
     # Scheduler Side Methods
@@ -302,7 +303,7 @@ def request_finished(
 class NixlConnectorWorker:
     """Implementation of Worker side methods"""
 
-    def __init__(self, engine_id: str):
+    def __init__(self, vllm_config: VllmConfig, engine_id: str):
         if NixlWrapper is None:
             logger.error("NIXL is not available")
             raise RuntimeError("NIXL is not available")
@@ -329,6 +330,7 @@ def __init__(self, engine_id: str):
         # Number of NIXL regions. Currently one region per cache
         # (so 1 per layer for MLA, otherwise 2 per layer)
         self.num_regions = 0
+        self.num_layers = 0
 
         # nixl_prepped_dlist_handle (int).
         self.src_xfer_side_handle: int = 0
@@ -355,6 +357,14 @@ def __init__(self, engine_id: str):
         # Background thread for establishing new connections.
         self._nixl_handshake_listener_t: Optional[threading.Thread] = None
 
+        self.vllm_config = vllm_config
+        self.block_size = vllm_config.cache_config.block_size
+
+        # TODO(mgoin): remove this once we have hybrid memory allocator
+        # Optimization for models with local attention (Llama 4)
+        # List of block window sizes for each layer for local attention
+        self.block_window_per_layer: list[Optional[int]] = []
+
     @staticmethod
     def _nixl_handshake_listener(metadata: NixlAgentMetadata,
                                  ready_event: threading.Event, rank: int):
@@ -465,6 +475,27 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                 kv_caches_base_addr.append(base_addr)
         self.kv_caches_base_addr[self.engine_id] = kv_caches_base_addr
         self.num_regions = len(caches_data)
+        self.num_layers = len(self.kv_caches.keys())
+
+        # TODO(mgoin): remove this once we have hybrid memory allocator
+        # Optimization for models with local attention (Llama 4)
+        if self.vllm_config.model_config.hf_config.model_type == "llama4":
+            from transformers import Llama4TextConfig
+            assert isinstance(self.vllm_config.model_config.hf_text_config,
+                              Llama4TextConfig)
+            llama4_config = self.vllm_config.model_config.hf_text_config
+            no_rope_layers = llama4_config.no_rope_layers
+            chunk_size = llama4_config.attention_chunk_size
+            chunk_block_size = math.ceil(chunk_size / self.block_size)
+            for layer_idx in range(self.num_layers):
+                # no_rope_layers[layer_idx] == 0 means NoPE (global)
+                # Any other value means RoPE (local chunked)
+                is_local_attention = no_rope_layers[layer_idx] != 0
+                block_window = chunk_block_size if is_local_attention else None
+                self.block_window_per_layer.append(block_window)
+            logger.debug("Llama 4 block window per layer mapping: %s",
+                         self.block_window_per_layer)
+            assert len(self.block_window_per_layer) == self.num_layers
 
         descs = self.nixl_wrapper.get_reg_descs(caches_data, "VRAM")
         logger.debug("Registering descs: %s", caches_data)
@@ -699,10 +730,39 @@ def _read_blocks(
         remote_xfer_side_handle = self.dst_xfer_side_handles[dst_engine_id]
 
         # Get descs ids.
-        remote_block_descs_ids = self._get_block_descs_ids(
-            dst_engine_id, remote_block_ids)
-        local_block_descs_ids = self._get_block_descs_ids(
-            self.engine_id, local_block_ids)
+        local_block_descs_ids: list[int] = []
+        remote_block_descs_ids: list[int] = []
+        if not self.block_window_per_layer:
+            # Default case: assume global attention
+            remote_block_descs_ids = self._get_block_descs_ids(
+                dst_engine_id, remote_block_ids)
+            local_block_descs_ids = self._get_block_descs_ids(
+                self.engine_id, local_block_ids)
+        else:
+            # TODO(mgoin): remove this once we have hybrid memory allocator
+            # Optimization for models with local attention (Llama 4)
+            for layer_idx, block_window in enumerate(
+                    self.block_window_per_layer):
+                # For each layer:
+                if block_window is None:
+                    # If not chunked, we just use the
+                    # full block lists (global attention)
+                    layer_local_block_ids = local_block_ids
+                    layer_remote_block_ids = remote_block_ids
+                else:
+                    # If chunked, get the last block_window blocks
+                    layer_local_block_ids = local_block_ids[-block_window:]
+                    layer_remote_block_ids = remote_block_ids[-block_window:]
+
+                # Get descs ids for the layer.
+                layer_local_desc_ids = self._get_block_descs_ids(
+                    self.engine_id, layer_local_block_ids, layer_idx)
+                layer_remote_desc_ids = self._get_block_descs_ids(
+                    dst_engine_id, layer_remote_block_ids, layer_idx)
+
+                local_block_descs_ids.extend(layer_local_desc_ids)
+                remote_block_descs_ids.extend(layer_remote_desc_ids)
+
         assert len(local_block_descs_ids) == len(remote_block_descs_ids)
 
         # Prepare transfer with Nixl.
@@ -721,12 +781,31 @@ def _read_blocks(
         # Use handle to check completion in future step().
         self._recving_transfers[request_id].append(handle)
 
-    def _get_block_descs_ids(self, engine_id: str,
-                             block_ids: list[int]) -> list[int]:
-        """Get the descs ids for a set of block ids."""
+    def _get_block_descs_ids(self,
+                             engine_id: str,
+                             block_ids: list[int],
+                             layer_idx: Optional[int] = None) -> list[int]:
+        """
+        Get the descs ids for a set of block ids.
+        If layer_idx is provided, we use the region_ids for the given layer.
+        Otherwise, we use all regions.
+        """
+
+        if layer_idx is None:
+            region_ids = range(self.num_regions)
+        else:
+            assert layer_idx < self.num_layers
+            if self.num_layers < self.num_regions:
+                # If we have more regions than layers, we assume that
+                # the regions are organized as [K0, V0, K1, V1, ...]
+                # and we select K_i and V_i
+                assert 2 * self.num_layers == self.num_regions
+                region_ids = range(2 * layer_idx, 2 * layer_idx + 2)
+            else:
+                # Otherwise, we assume we have MLA and select i-th layer
+                assert self.num_layers == self.num_regions
+                region_ids = range(layer_idx, layer_idx + 1)
 
-        # range(1) for MLA, range(2) otherwise.
-        region_ids = range(self.num_regions)
         num_blocks = self.dst_num_blocks[engine_id]
 
         # Compute the desc ids for each block.