[Nixl] Minor refactor to handshake related metadata (#26410)

Signed-off-by: NickLucche <nlucches@redhat.com>

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -565,8 +565,6 @@ class TestNixlHandshake:
                 kv_cache_layout=mismatched_layout,
             )
 
-            # We don't check layout for homogeneous TP and MLA for now, as the
-            # whole block is moved.
             with pytest.raises(RuntimeError):
                 # mismatched layout is expected to fail
                 worker.add_remote_agent(meta, remote_tp_size=2)

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

@@ -36,7 +36,6 @@ from vllm.distributed.parallel_state import (
     get_tensor_model_parallel_world_size,
     get_tp_group,
 )
-from vllm.distributed.utils import divide
 from vllm.forward_context import ForwardContext
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
@@ -521,6 +520,72 @@ class NixlConnectorScheduler:
 class NixlConnectorWorker:
     """Implementation of Worker side methods"""
 
+    @dataclass
+    class TpKVTopology:
+        """
+        Helper class for tensor parallel and KV topology information for
+        mapping between local and remote TP workers.
+        """
+
+        tp_size: int
+        tp_rank: int
+        remote_tp_size: dict[EngineId, int]
+        is_mla: bool
+        total_num_kv_heads: int
+
+        def tp_ratio(
+            self,
+            remote_tp_size: int,
+        ) -> int:
+            """
+            Calculate the tensor parallel ratio between local and remote TP.
+            We can think of it as the number of local TP workers-per-remote TP
+            workers. Local workers will read from the same remote TP worker in
+            groups of size `tp_ratio`.
+            """
+            assert self.tp_size % remote_tp_size == 0, (
+                f"Local tensor parallel size {self.tp_size} is not divisible "
+                f"by remote tensor parallel size {remote_tp_size}."
+            )
+            return self.tp_size // remote_tp_size
+
+        def tp_ratio_from_engine_id(
+            self,
+            remote_engine_id: EngineId,
+        ) -> int:
+            remote_tp_size = self.remote_tp_size[remote_engine_id]
+            return self.tp_ratio(remote_tp_size)
+
+        def is_kv_replicated(self, engine_id: EngineId) -> bool:
+            """
+            Whether the KV cache is replicated across TP workers due to the
+            number of TP workers being greater than the number of KV heads.
+            """
+            tp_size = self.remote_tp_size[engine_id]
+            return tp_size // self.total_num_kv_heads >= 1
+
+        def replicates_kv_cache(self, remote_engine_id: EngineId) -> bool:
+            # MLA is always replicated as the hidden dim can't be split.
+            return self.is_mla or self.is_kv_replicated(remote_engine_id)
+
+        def get_target_remote_rank(
+            self,
+            remote_tp_size: int,
+        ) -> int:
+            """
+            Get the remote TP rank (on P) that the current local TP rank
+            (on D) will read from.
+            """
+            tp_ratio = self.tp_ratio(remote_tp_size)
+            return self.tp_rank // tp_ratio
+
+        def get_target_remote_rank_from_engine_id(
+            self,
+            remote_engine_id: EngineId,
+        ) -> int:
+            remote_tp_size = self.remote_tp_size[remote_engine_id]
+            return self.get_target_remote_rank(remote_tp_size)
+
     def __init__(self, vllm_config: VllmConfig, engine_id: str):
         if NixlWrapper is None:
             logger.error("NIXL is not available")
@@ -534,6 +599,7 @@ class NixlConnectorWorker:
 
         if vllm_config.kv_transfer_config is None:
             raise ValueError("kv_transfer_config must be set for NixlConnector")
+        self.kv_transfer_config = vllm_config.kv_transfer_config
 
         self.nixl_backends = vllm_config.kv_transfer_config.get_from_extra_config(
             "backends", ["UCX"]
@@ -654,7 +720,6 @@ class NixlConnectorWorker:
         # Protects _handshake_futures and _remote_agents.
         self._handshake_lock = threading.RLock()
 
-        self.vllm_config = vllm_config
         self.block_size = vllm_config.cache_config.block_size
         self.model_config = vllm_config.model_config
         self.cache_config = vllm_config.cache_config
@@ -686,6 +751,14 @@ class NixlConnectorWorker:
         self.consumer_notification_counts_by_req = defaultdict[ReqId, int](int)
         self.xfer_stats = NixlKVConnectorStats()
 
+        self.kv_topo = self.TpKVTopology(
+            tp_size=self.world_size,
+            tp_rank=self.tp_rank,
+            remote_tp_size=self._tp_size,  # shared state
+            is_mla=self.use_mla,
+            total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
+        )
+
     @staticmethod
     def _nixl_handshake_listener(
         metadata: NixlAgentMetadata,
@@ -731,8 +804,7 @@ class NixlConnectorWorker:
 
         # Handshake only with the remote TP rank that current local rank will
         # pull from. With homogeneous TP it happens to be the same rank_i.
-        tp_ratio = self._tp_size[self.engine_id] // remote_tp_size
-        p_remote_rank = self.tp_rank // tp_ratio
+        p_remote_rank = self.kv_topo.get_target_remote_rank(remote_tp_size)
         path = make_zmq_path("tcp", host, port + p_remote_rank)
         logger.debug(
             "Querying metadata on path: %s at remote rank %s", path, p_remote_rank
@@ -989,13 +1061,11 @@ class NixlConnectorWorker:
 
         # TODO(mgoin): Hybrid memory allocator is currently disabled for
         # models with local attention (Llama 4). Can remove this once enabled.
-        if self.vllm_config.model_config.hf_config.model_type == "llama4":
+        if self.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
+            assert isinstance(self.model_config.hf_text_config, Llama4TextConfig)
+            llama4_config = self.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)
@@ -1078,36 +1148,106 @@ class NixlConnectorWorker:
         engine_id = nixl_agent_meta.engine_id
         # TODO re-evaluate refreshing for scaling/recovery
         if remote_tp_rank in self._remote_agents.get(engine_id, {}):
+            logger.debug(
+                "Remote agent with engine_id %s and rank"
+                "%s already exchanged metadata, skip handshake.",
+                engine_id,
+                remote_tp_rank,
+            )
             return self._remote_agents[engine_id][remote_tp_rank]
 
+        ### Register remote agent metadata
         if engine_id not in self._tp_size:
             self._tp_size[engine_id] = remote_tp_size
-        else:
-            assert self._tp_size[engine_id] == remote_tp_size
-        # TODO We may eventually want to skip enforcing the same attn backend.
-        assert nixl_agent_meta.attn_backend_name == self.backend_name
 
         remote_agent_name = self.nixl_wrapper.add_remote_agent(
             nixl_agent_meta.agent_metadata
         )
 
+        # Handle tp_size>num_kv_heads: replicate KV cache.
+        replicates_kv_cache = self.kv_topo.replicates_kv_cache(engine_id)
+
+        # Create dst descs and xfer side handles. TP workers have same #blocks
+        # so we only register once per engine_id.
+        if engine_id not in self.dst_num_blocks:
+            self.dst_num_blocks[engine_id] = nixl_agent_meta.num_blocks
+
+        # Keep track of remote agent kv caches base addresses.
+        self.kv_caches_base_addr[engine_id] = nixl_agent_meta.kv_caches_base_addr
+
+        self._validate_remote_agent_handshake(nixl_agent_meta, remote_tp_size)
+
         # Number of D TP workers reading from a single P TP worker. This is
         # 1 when P and D `--tensor-parallel-size` match.
-        tp_ratio = divide(self._tp_size[self.engine_id], self._tp_size[engine_id])
+        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(engine_id)
+
+        ### Register remote agent memory regions
+        blocks_data = []
+        # With homogeneous TP, D pulls the whole kv cache from corresponding
+        # rank. With heterogeneous TP, prepare the descriptors by splitting the
+        # P KV cache along kv_head dim, of D worker's kv_head size (D>P).
+        # Eg. PTP1 DTP2 => P0 KV:[block0-KV_0 | block0-KV_1..].
+
+        # Register all remote blocks, but only the corresponding kv heads.
+        for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
+            kv_block_len = self.get_backend_aware_kv_block_len(layer_idx=i)
+            rank_offset = (
+                self.tp_rank % tp_ratio * kv_block_len if not replicates_kv_cache else 0
+            )
+            for block_id in range(nixl_agent_meta.num_blocks):
+                block_offset = block_id * nixl_agent_meta.block_lens[i]
+                # For each block, grab the heads chunk belonging to rank_i
+                # of size remote_nheads // tp_ratio, which correspond to
+                # self.block_len == remote_block_len//tp_ratio bytes.
+                addr = base_addr + block_offset + rank_offset
+                # (addr, len, device id)
+                blocks_data.append((addr, kv_block_len, remote_tp_rank))
+
+            if self._use_flashinfer:
+                # With FlashInfer index V separately to allow head splitting.
+                for block_id in range(nixl_agent_meta.num_blocks):
+                    block_offset = block_id * nixl_agent_meta.block_lens[i]
+                    addr = base_addr + block_offset + rank_offset
+                    v_addr = addr + nixl_agent_meta.block_lens[i] // 2
+                    blocks_data.append((v_addr, kv_block_len, remote_tp_rank))
+
+        logger.debug(
+            "Created %s blocks for dst engine %s with remote rank %s and local rank %s",
+            len(blocks_data),
+            engine_id,
+            remote_tp_rank,
+            self.tp_rank,
+        )
+
+        # Register with NIXL.
+        descs = self.nixl_wrapper.get_xfer_descs(blocks_data, self.nixl_memory_type)
+        self.dst_xfer_side_handles[engine_id] = self.nixl_wrapper.prep_xfer_dlist(
+            remote_agent_name, descs
+        )
+
+        return remote_agent_name
+
+    def _validate_remote_agent_handshake(
+        self, nixl_agent_meta: NixlAgentMetadata, remote_tp_size: int
+    ):
+        """
+        Validate the remote agent handshake metadata ensuring the
+        invariants hold true.
+        """
+        remote_engine_id = nixl_agent_meta.engine_id
+
+        assert self._tp_size[remote_engine_id] == remote_tp_size
+        # TODO We may eventually want to skip enforcing the same attn backend.
+        assert nixl_agent_meta.attn_backend_name == self.backend_name
+
+        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(remote_engine_id)
         assert tp_ratio > 0, "Decode TP cannot be smaller than prefill TP"
         assert not self._use_pallas or tp_ratio == 1, (
             "TPU (pallas_v1) DOES NOT support heterogeneous TP yet."
         )
-
-        # Handle tp_size>num_kv_heads: replicate KV cache.
-        total_num_kv_heads = self.model_config.get_total_num_kv_heads()
-        is_kv_replicated = self._tp_size[engine_id] // total_num_kv_heads >= 1
-
-        remote_block_len = nixl_agent_meta.block_lens[0]
-        if nixl_agent_meta.kv_cache_layout != self.kv_cache_layout:
+        if not self.use_mla and nixl_agent_meta.kv_cache_layout != self.kv_cache_layout:
             if (
-                self.vllm_config.kv_transfer_config is not None
-                and self.vllm_config.kv_transfer_config.enable_permute_local_kv
+                self.kv_transfer_config.enable_permute_local_kv
                 and nixl_agent_meta.kv_cache_layout == "HND"
             ):
                 logger.info(
@@ -1121,13 +1261,19 @@ class NixlConnectorWorker:
                     "Or enable experimental feature to use HND to NHD support by "
                     "setting 'enable_permute_local_kv'=True in --kv-transfer-config."
                 )
-        if self.use_mla or is_kv_replicated:
+
+        # Block len can only vary across layers when using MLA.
+        remote_block_len = nixl_agent_meta.block_lens[0]
+        if self.use_mla or self.kv_topo.is_kv_replicated(remote_engine_id):
             # With replicated KV cache, only the number of blocks can differ.
             assert self.block_len_per_layer == nixl_agent_meta.block_lens, (
                 "KV cache sizes must match between P and D when replicated"
             )
             remote_block_size = remote_block_len // (self.slot_size_per_layer[0])
         else:
+            if tp_ratio > 1 and self.device_type == "xpu":
+                # XPU uses NHD, hence it does not support splitting on H
+                raise ValueError("Heterogeneous TP is not supported on XPU")
             # When MLA is not used, this is a list of the same block length
             for block_len in nixl_agent_meta.block_lens:
                 assert block_len == remote_block_len, (
@@ -1139,14 +1285,6 @@ class NixlConnectorWorker:
             if self._use_flashinfer:
                 # With flashinfer, KV are sent in the same message.
                 remote_block_size //= 2
-            if tp_ratio > 1:
-                # Heterogeneous TP expects same kv_cache_layout.
-                if nixl_agent_meta.kv_cache_layout == "NHD":
-                    raise ValueError(
-                        "Heterogeneous TP is not supported for remote with NHD."
-                    )
-                if self.device_type == "xpu":
-                    raise ValueError("Heterogeneous TP is not supported on XPU")
 
             assert remote_block_len == self.block_len_per_layer[0] * tp_ratio, (
                 "Remote P worker KV layer cache must be of shape [2, N, "
@@ -1158,60 +1296,10 @@ class NixlConnectorWorker:
             f"{self.block_size=}, {remote_block_size=}"
         )
 
-        # Create dst descs and xfer side handles. TP workers have same #blocks.
-        if engine_id in self.dst_num_blocks:
-            assert self.dst_num_blocks[engine_id] == nixl_agent_meta.num_blocks
-        else:
-            self.dst_num_blocks[engine_id] = nixl_agent_meta.num_blocks
-
-        blocks_data = []
-        # With homogeneous TP, D pulls the whole kv cache from corresponding
-        # rank. With heterogeneous TP, prepare the descriptors by splitting the
-        # P KV cache along kv_head dim, of D worker's kv_head size (D>P).
-        # Eg. PTP1 DTP2 => P0 KV:[block0-KV_0 | block0-KV_1..].
-        self.kv_caches_base_addr[engine_id] = nixl_agent_meta.kv_caches_base_addr
+        # TP workers have same #blocks.
+        assert self.dst_num_blocks[remote_engine_id] == nixl_agent_meta.num_blocks
 
         assert len(nixl_agent_meta.kv_caches_base_addr) == len(self.block_len_per_layer)
-        # Register all remote blocks, but only the corresponding kv heads.
-        for i, base_addr in enumerate(nixl_agent_meta.kv_caches_base_addr):
-            kv_block_len = self.get_backend_aware_kv_block_len(layer_idx=i)
-            rank_offset = (
-                self.tp_rank % tp_ratio * kv_block_len
-                if not (self.use_mla or is_kv_replicated)
-                else 0
-            )
-            for block_id in range(nixl_agent_meta.num_blocks):
-                block_offset = block_id * nixl_agent_meta.block_lens[i]
-                # For each block, grab the heads chunk belonging to rank_i
-                # of size remote_nheads // tp_ratio, which correspond to
-                # self.block_len == remote_block_len//tp_ratio bytes.
-                addr = base_addr + block_offset + rank_offset
-                # (addr, len, device id)
-                blocks_data.append((addr, kv_block_len, remote_tp_rank))
-
-            if self._use_flashinfer:
-                # With FlashInfer index V separately to allow head splitting.
-                for block_id in range(nixl_agent_meta.num_blocks):
-                    block_offset = block_id * nixl_agent_meta.block_lens[i]
-                    addr = base_addr + block_offset + rank_offset
-                    v_addr = addr + nixl_agent_meta.block_lens[i] // 2
-                    blocks_data.append((v_addr, kv_block_len, remote_tp_rank))
-
-        logger.debug(
-            "Created %s blocks for dst engine %s with remote rank %s and local rank %s",
-            len(blocks_data),
-            engine_id,
-            remote_tp_rank,
-            self.tp_rank,
-        )
-
-        # Register with NIXL.
-        descs = self.nixl_wrapper.get_xfer_descs(blocks_data, self.nixl_memory_type)
-        self.dst_xfer_side_handles[engine_id] = self.nixl_wrapper.prep_xfer_dlist(
-            remote_agent_name, descs
-        )
-
-        return remote_agent_name
 
     def sync_recved_kv_to_device(self, req_id: str, meta: ReqMeta):
         """copy recved kv from host buffer to device."""
@@ -1505,14 +1593,16 @@ class NixlConnectorWorker:
 
         # Number of D TP workers that will read from dst P. Propagate tp_ratio
         # on notification so that dst worker can wait before freeing blocks.
-        tp_ratio = self._tp_size[self.engine_id] // self._tp_size[dst_engine_id]
+        tp_ratio = self.kv_topo.tp_ratio_from_engine_id(dst_engine_id)
         notif_id = f"{request_id}:{tp_ratio}".encode()
 
         # Full prefix cache hit: do not need to read remote blocks,
         # just notify P worker that we have the blocks we need.
         num_local_blocks = len(local_block_ids)
         if num_local_blocks == 0:
-            remote_rank = self.tp_rank // tp_ratio
+            remote_rank = self.kv_topo.get_target_remote_rank_from_engine_id(
+                dst_engine_id
+            )
             agent_name = self._remote_agents[dst_engine_id][remote_rank]
             try:
                 self.nixl_wrapper.send_notif(agent_name, notif_msg=notif_id)