[NIXL] Add support for MLA caches with different latent dim

tests/v1/kv_connector/unit/test_nixl_connector.py

@@ -255,8 +255,9 @@ def _nixl_handshake(self, host: str, port: int, remote_tp_size: int,
         time.sleep(self._hand_shake_latency)
         # These should've been done in register_kv_caches(), called by
         # gpu_model_runner. Here we just hardcode some dummy values.
-        self.slot_size_bytes = 4096
-        self.block_len = self.slot_size_bytes * self.block_size
+        slot_size_bytes = 4096
+        self.slot_size_per_layer = [slot_size_bytes]
+        self.block_len_per_layer = [slot_size_bytes * self.block_size]
         self.num_blocks = 1
         self.dst_num_blocks[self.engine_id] = self.num_blocks
 
@@ -268,7 +269,7 @@ def _nixl_handshake(self, host: str, port: int, remote_tp_size: int,
                 agent_metadata=FakeNixlWrapper.AGENT_METADATA,
                 kv_caches_base_addr=[0],
                 num_blocks=1,
-                block_len=self.block_len,
+                block_lens=self.block_len_per_layer,
                 attn_backend_name=self.backend_name,
                 # `self.kv_cache_layout` is only forced to HND when vllm engine
                 # is started. We mock HND here.
@@ -485,8 +486,8 @@ def test_handshake_fails_on_kv_cache_layout_mismatch(self, dist_init):
             worker = connector.connector_worker
 
             # Minimal local registration params used by add_remote_agent
-            worker.slot_size_bytes = 4096
-            worker.block_len = worker.slot_size_bytes * worker.block_size
+            worker.slot_size_per_layer = [4096]
+            worker.block_len_per_layer = [4096 * worker.block_size]
             worker.num_blocks = 1
             worker.dst_num_blocks[worker.engine_id] = worker.num_blocks
 
@@ -498,7 +499,7 @@ def test_handshake_fails_on_kv_cache_layout_mismatch(self, dist_init):
                 agent_metadata=FakeNixlWrapper.AGENT_METADATA,
                 kv_caches_base_addr=[0],
                 num_blocks=1,
-                block_len=worker.block_len,
+                block_lens=worker.block_len_per_layer,
                 attn_backend_name=worker.backend_name,
                 kv_cache_layout=mismatched_layout,
             )

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

@@ -87,7 +87,7 @@ class NixlAgentMetadata(
     agent_metadata: bytes
     kv_caches_base_addr: list[int]
     num_blocks: int
-    block_len: int
+    block_lens: list[int]
     attn_backend_name: str
     kv_cache_layout: str
 
@@ -772,6 +772,9 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         split_k_and_v = not (self.use_mla or self._use_pallas
                              or self._use_flashinfer)
         tensor_size_bytes = None
+        # Enable different block lengths for different layers when MLA is used.
+        self.block_len_per_layer = list[int]()
+        self.slot_size_per_layer = list[int]()  # HD bytes in kv terms
         for layer_name, cache_or_caches in xfer_buffers.items():
             cache_list = cache_or_caches if split_k_and_v else [
                 cache_or_caches
@@ -789,10 +792,25 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                     tensor_size_bytes = curr_tensor_size_bytes
                     self.num_blocks = cache.shape[0]
 
-                assert tensor_size_bytes == curr_tensor_size_bytes, \
-                    "All kv cache tensors must have the same size"
+                assert cache.shape[0] == self.num_blocks, \
+                    "All kv cache tensors must have the same number of blocks"
+
+                self.block_len_per_layer.append(curr_tensor_size_bytes //
+                                                self.num_blocks)
+                self.slot_size_per_layer.append(self.block_len_per_layer[-1] //
+                                                self.block_size)
+
+                if not self.use_mla:
+                    # Different kv cache shape is not supported by HeteroTP
+                    assert tensor_size_bytes == curr_tensor_size_bytes, \
+                        "All kv cache tensors must have the same size"
                 caches_data.append(
-                    (base_addr, tensor_size_bytes, self.tp_rank, ""))
+                    (base_addr, curr_tensor_size_bytes, self.tp_rank, ""))
+
+        logger.debug("Different block lengths collected: %s",
+                     set(self.block_len_per_layer))
+        assert len(self.block_len_per_layer) == len(seen_base_addresses)
+        assert self.num_blocks != 0
 
         self.kv_caches_base_addr[self.engine_id] = seen_base_addresses
         self.num_regions = len(caches_data)
@@ -805,16 +823,12 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         logger.debug("Done registering descs")
         self._registered_descs.append(descs)
 
-        assert tensor_size_bytes is not None
-        assert self.num_blocks != 0
-        assert tensor_size_bytes % self.num_blocks == 0
-        self.block_len = tensor_size_bytes // self.num_blocks
-        self.slot_size_bytes = self.block_len // self.block_size
         self.device_kv_caches = kv_caches
         self.dst_num_blocks[self.engine_id] = self.num_blocks
         if self._use_flashinfer:
-            assert self.slot_size_bytes % 2 == 0
-            self.slot_size_bytes /= 2
+            for i in range(len(self.slot_size_per_layer)):
+                assert self.slot_size_per_layer[i] % 2 == 0
+                self.slot_size_per_layer[i] //= 2
 
             # NOTE (NickLucche) When FlashInfer is used, memory is registered
             # with joint KV for each block. This minimizes the overhead in
@@ -824,17 +838,17 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             # of 'virtual' regions here and halve `block_len` below.
             self.num_regions *= 2
 
-        kv_block_len = self.get_backend_aware_kv_block_len()
         # Register local/src descr for NIXL xfer.
         blocks_data = []
-        for base_addr in seen_base_addresses:
+        for i, base_addr in enumerate(seen_base_addresses):
+            kv_block_len = self.get_backend_aware_kv_block_len(layer_idx=i)
             # NOTE With heter-TP, more blocks are prepared than what are
             # needed as self.num_blocks >= nixl_agent_meta.num_blocks. We
             # could create fewer, but then _get_block_descs_ids needs to
             # select agent_meta.num_blocks instead of self.num_blocks for
             # local descr, and that makes handling regular flow less clean.
             for block_id in range(self.num_blocks):
-                block_offset = block_id * self.block_len
+                block_offset = block_id * self.block_len_per_layer[i]
                 addr = base_addr + block_offset
                 # (addr, len, device id)
                 blocks_data.append((addr, kv_block_len, self.tp_rank))
@@ -844,7 +858,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
                 # descs ordering. This is needed for selecting contiguous heads
                 # when split across TP ranks.
                 for block_id in range(self.num_blocks):
-                    block_offset = block_id * self.block_len
+                    block_offset = block_id * self.block_len_per_layer[i]
                     addr = base_addr + block_offset
                     # Register addresses for V cache (K registered first).
                     v_addr = addr + kv_block_len
@@ -884,7 +898,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             agent_metadata=self.nixl_wrapper.get_agent_metadata(),
             kv_caches_base_addr=self.kv_caches_base_addr[self.engine_id],
             num_blocks=self.num_blocks,
-            block_len=self.block_len,
+            block_lens=self.block_len_per_layer,
             attn_backend_name=self.backend_name,
             kv_cache_layout=self.kv_cache_layout)
         ready_event = threading.Event()
@@ -909,7 +923,7 @@ def add_remote_agent(self,
         The latter, assuming D.world_size > P.world_size, requires that two or 
         more local TP worker share the xfer from a single TP worker.
 
-        Here's an example:
+        Here's an example (non-MLA case):
 
         rank_offset     p_remote_tp_rank
         (kv split no)    
@@ -965,14 +979,20 @@ def add_remote_agent(self,
         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 self.use_mla or is_kv_replicated:
-            # With MLA the only difference is in the number of blocks.
-            remote_block_size = nixl_agent_meta.block_len // (
-                self.slot_size_bytes)
-            assert self.block_len == nixl_agent_meta.block_len
+            # 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:
-            remote_block_size = nixl_agent_meta.block_len // (
-                self.slot_size_bytes * tp_ratio)
+            # 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, \
+                    "All remote layers must have the same block size"
+            remote_block_size = remote_block_len // (
+                self.slot_size_per_layer[0] * tp_ratio)
             if self._use_flashinfer:
                 # With flashinfer, KV are sent in the same message.
                 remote_block_size //= 2
@@ -983,14 +1003,14 @@ def add_remote_agent(self,
                     raise ValueError(
                         "Heterogeneous TP is not supported on XPU")
 
-            assert nixl_agent_meta.block_len == self.block_len * tp_ratio, (
+            assert remote_block_len == self.block_len_per_layer[0] * tp_ratio, (
                 "Remote P worker KV layer cache must be of shape [2, N, "
                 "local_kv_heads*tp_ratio, block_size, head_dim] and same dtype."
             )
 
         assert self.block_size == remote_block_size, (
-            "Remote P worker with different block size is not supported "
-            f"{self.block_size=} {remote_block_size=}")
+            "Remote P worker with different page/block size is not supported "
+            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:
@@ -1005,13 +1025,16 @@ def add_remote_agent(self,
         # 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
-        kv_block_len = self.get_backend_aware_kv_block_len()
-        rank_offset = self.tp_rank % tp_ratio * kv_block_len \
-            if not (self.use_mla or is_kv_replicated) else 0
+
+        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 base_addr in nixl_agent_meta.kv_caches_base_addr:
+        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_len
+                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.
@@ -1022,9 +1045,9 @@ def add_remote_agent(self,
             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_len
+                    block_offset = block_id * nixl_agent_meta.block_lens[i]
                     addr = base_addr + block_offset + rank_offset
-                    v_addr = addr + nixl_agent_meta.block_len // 2
+                    v_addr = addr + nixl_agent_meta.block_lens[i] // 2
                     blocks_data.append((v_addr, kv_block_len, remote_tp_rank))
 
         logger.debug(
@@ -1351,7 +1374,7 @@ def _get_block_descs_ids(self,
         descs_ids = region_ids * num_blocks + block_ids
         return descs_ids.flatten()
 
-    def get_backend_aware_kv_block_len(self):
+    def get_backend_aware_kv_block_len(self, layer_idx: int):
         """
         Get the block length for one K/V element (K and V have the same size).
 
@@ -1362,9 +1385,9 @@ def get_backend_aware_kv_block_len(self):
         """
         if self._use_flashinfer:
             # For indexing only half (either just the K or V part).
-            block_len = self.block_len // 2
+            block_len = self.block_len_per_layer[layer_idx] // 2
         else:
-            block_len = self.block_len
+            block_len = self.block_len_per_layer[layer_idx]
         return block_len
 
     def get_kv_connector_stats(self) -> Optional[KVConnectorStats]:

vllm/v1/core/kv_cache_utils.py

@@ -1103,7 +1103,9 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
         kv_cache_spec: The kv cache spec of each attention layer in the model
     """
 
-    if is_kv_cache_spec_uniform(kv_cache_spec):
+    if is_kv_cache_spec_uniform(
+            kv_cache_spec) or UniformTypeKVCacheSpecs.is_uniform_type(
+                kv_cache_spec):
         return
 
     logger.warning(
@@ -1141,7 +1143,8 @@ def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
                     attention_chunk_size=spec.attention_chunk_size,
                 )
 
-    if not is_kv_cache_spec_uniform(kv_cache_spec):
+    if not (is_kv_cache_spec_uniform(kv_cache_spec)
+            or UniformTypeKVCacheSpecs.is_uniform_type(kv_cache_spec)):
         raise ValueError("Hybrid KV cache manager is disabled but failed to "
                          "convert the KV cache specs to one unified type.")