[DCP] Support dcp kv_cache interleave size > 1 (#26696)

Signed-off-by: zhangsicheng5 <zhangsicheng5@huawei.com>
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
Signed-off-by: Qiu <qiuchunshuo@huawei.com>
Co-authored-by: QiuChunshuo <qiuchunshuo@huawei.com>

tests/distributed/test_context_parallel.py

@@ -30,6 +30,7 @@ class ParallelSetup(NamedTuple):
     tp_size: int
     pp_size: int
     dcp_size: int
+    dcp_kv_cache_interleave_size: int
     eager_mode: bool
     chunked_prefill: bool
 
@@ -52,6 +53,7 @@ class CPTestSettings:
         tp_base: int = 4,
         pp_base: int = 1,
         dcp_base: int = 1,
+        dcp_kv_cache_interleave_size: int = 1,
         multi_node_only: bool = False,
         runner: RunnerOption = "auto",
         load_format: str | None = None,
@@ -66,6 +68,7 @@ class CPTestSettings:
                                 tp_size=tp_base,
                                 pp_size=pp_multiplier * pp_base,
                                 dcp_size=int(dcp_multiplier * tp_base),
+                                dcp_kv_cache_interleave_size=dcp_kv_cache_interleave_size,
                                 eager_mode=eager_mode_val,
                                 chunked_prefill=chunked_prefill_val,
                             )
@@ -108,6 +111,7 @@ def _compare_cp_with_tp(
         tp_size,
         pp_size,
         dcp_size,
+        dcp_kv_cache_interleave_size,
         eager_mode,
         chunked_prefill,
     ) = parallel_setup
@@ -180,6 +184,8 @@ def _compare_cp_with_tp(
         str(pp_size),
         "--decode-context-parallel-size",
         str(dcp_size),
+        "--dcp-kv-cache-interleave-size",
+        str(dcp_kv_cache_interleave_size),
         "--distributed-executor-backend",
         distributed_backend,
     ]
@@ -207,6 +213,7 @@ CP_TEXT_GENERATION_MODELS = {
     "deepseek-ai/DeepSeek-V2-Lite-Chat": [
         CPTestSettings.detailed(),
         CPTestSettings.detailed(tp_base=2),
+        CPTestSettings.detailed(tp_base=2, dcp_kv_cache_interleave_size=64),
     ],
     "bigcode/gpt_bigcode-santacoder": [
         CPTestSettings.detailed(),

tests/v1/worker/test_gpu_model_runner.py

@@ -951,6 +951,7 @@ def test_hybrid_block_table_initialization():
     max_num_reqs = 10
     max_num_blocks_per_req = 20
     max_num_batched_tokens = 512
+    dcp_kv_cache_interleave_size = 8
 
     block_table = BlockTable(
         block_size=block_size,
@@ -960,6 +961,7 @@ def test_hybrid_block_table_initialization():
         pin_memory=False,
         device=torch.device(DEVICE),
         kernel_block_size=kernel_block_sizes[0],
+        dcp_kv_cache_interleave_size=dcp_kv_cache_interleave_size,
     )
 
     # Verify hybrid block configuration

vllm/attention/ops/common.py

@@ -53,6 +53,7 @@ def _correct_attn_cp_out_kernel(
     lse = tl.load(lses_ptr + lse_offsets)
     lse = tl.where((lse != lse) | (lse == float("inf")), -float("inf"), lse)
     lse_max = tl.max(lse, axis=0)
+    lse_max = tl.where(lse_max == -float("inf"), 0, lse_max)
     lse -= lse_max
     lse_exp = tl.exp(lse)
     lse_acc = tl.sum(lse_exp, axis=0)

vllm/config/parallel.py

@@ -227,6 +227,17 @@ class ParallelConfig:
     not change by dcp, it simply reuse the GPUs of TP group, and tp_size
     needs to be divisible by dcp_size."""
 
+    dcp_kv_cache_interleave_size: int = 1
+    """Interleave size of kv_cache storage while using dcp or cp > 1,
+    store interleave_size tokens on (d)cp i,
+    then store next interleave_size tokens on (d)cp i+1.
+    Interleave_size=1: token-level align, token i is stored on rank i % (d)cp_size.
+    Interleave_size=block_size: block-level align, first fill the block on first rank,
+    token is stored on rank i+1 block j after rank i block j is full.
+    Block_size should be greater than or equal to dcp_kv_cache_interleave_size.
+    Block_size should be divisible by dcp_kv_cache_interleave_size.
+    """
+
     _api_process_count: int = Field(default=1, gt=0)
     """
     The number of API processes initialized.

vllm/config/vllm.py

@@ -608,6 +608,23 @@ class VllmConfig:
             )
         current_platform.check_and_update_config(self)
 
+        assert (
+            self.parallel_config.dcp_kv_cache_interleave_size
+            <= self.cache_config.block_size
+            and self.cache_config.block_size
+            % self.parallel_config.dcp_kv_cache_interleave_size
+            == 0
+        ), (
+            f"Block_size({self.cache_config.block_size}) should be "
+            "greater than or equal to and divisible by dcp_kv_cache_interleave_size "
+            f"({self.parallel_config.dcp_kv_cache_interleave_size})."
+        )
+
+        assert (
+            self.parallel_config.dcp_kv_cache_interleave_size == 1
+            or self.speculative_config is None
+        ), "MTP with dcp_kv_cache_interleave_size > 1 is not supported now."
+
         # Do this after all the updates to compilation_config.mode
         if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
             self.compilation_config.set_splitting_ops_for_v1()

vllm/engine/arg_utils.py

@@ -385,6 +385,7 @@ class EngineArgs:
     pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
     tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
     decode_context_parallel_size: int = ParallelConfig.decode_context_parallel_size
+    dcp_kv_cache_interleave_size: int = ParallelConfig.dcp_kv_cache_interleave_size
     data_parallel_size: int = ParallelConfig.data_parallel_size
     data_parallel_rank: int | None = None
     data_parallel_start_rank: int | None = None
@@ -750,6 +751,10 @@ class EngineArgs:
             "-dcp",
             **parallel_kwargs["decode_context_parallel_size"],
         )
+        parallel_group.add_argument(
+            "--dcp-kv-cache-interleave-size",
+            **parallel_kwargs["dcp_kv_cache_interleave_size"],
+        )
         parallel_group.add_argument(
             "--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]
         )
@@ -1518,6 +1523,7 @@ class EngineArgs:
             worker_cls=self.worker_cls,
             worker_extension_cls=self.worker_extension_cls,
             decode_context_parallel_size=self.decode_context_parallel_size,
+            dcp_kv_cache_interleave_size=self.dcp_kv_cache_interleave_size,
             _api_process_count=self._api_process_count,
             _api_process_rank=self._api_process_rank,
         )

vllm/v1/attention/backends/flash_attn.py

@@ -43,6 +43,7 @@ from vllm.v1.attention.backends.utils import (
     AttentionCGSupport,
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
+    get_dcp_local_seq_lens,
     get_kv_cache_layout,
 )
 from vllm.v1.kv_cache_interface import AttentionSpec
@@ -238,6 +239,10 @@ class FlashAttentionMetadataBuilder(AttentionMetadataBuilder[FlashAttentionMetad
             self.dcp_world_size = 1
             self.dcp_rank = 0
 
+        self.dcp_kv_cache_interleave_size = (
+            self.parallel_config.dcp_kv_cache_interleave_size
+        )
+
         self.use_full_cuda_graph = (
             self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         )
@@ -352,8 +357,12 @@ class FlashAttentionMetadataBuilder(AttentionMetadataBuilder[FlashAttentionMetad
                 - common_attn_metadata.query_start_loc_cpu[:-1]
             )
             dcp_context_kv_lens_cpu = seq_lens_cpu - query_kv_lens_cpu
-            dcp_context_kv_lens_cpu = dcp_context_kv_lens_cpu // self.dcp_world_size + (
+
-                self.dcp_rank <= (dcp_context_kv_lens_cpu - 1) % self.dcp_world_size
+            dcp_context_kv_lens_cpu = get_dcp_local_seq_lens(
+                dcp_context_kv_lens_cpu,
+                self.dcp_world_size,
+                self.dcp_rank,
+                self.dcp_kv_cache_interleave_size,
             )
             dcp_context_kv_lens = dcp_context_kv_lens_cpu.to(self.device)
             max_dcp_context_kv_len = dcp_context_kv_lens.max().item()

vllm/v1/attention/backends/mla/common.py

@@ -225,6 +225,7 @@ from vllm.utils.math_utils import cdiv, round_down
 from vllm.v1.attention.backends.utils import (
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
+    get_dcp_local_seq_lens,
     get_per_layer_parameters,
     infer_global_hyperparameters,
     split_decodes_and_prefills,
@@ -361,10 +362,9 @@ class MLACommonPrefillMetadata:
         workspace: torch.Tensor
 
         # for mla DCP
-        cp_chunk_seq_lens: list[list[int]] | None = None
+        padded_local_chunk_seq_lens: list[list[int]] | None = None
-        origin_context_lens: list[int] | None = None
+        local_context_lens_allranks: list[list[int]] | None = None
-        cp_cu_seq_lens: torch.Tensor | None = None
+        padded_local_cu_seq_lens: torch.Tensor | None = None
-        chunk_size: int | None = None
         cu_seq_lens_lst: list[list[int]] | None = None
 
     block_table: torch.Tensor
@@ -568,6 +568,8 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
             # DCP might not be initialized in testing
             self.dcp_world_size = 1
             self.dcp_rank = 0
+        self.dcp_local_block_size = parallel_config.dcp_kv_cache_interleave_size
+        self.dcp_virtual_block_size = self.dcp_local_block_size * self.dcp_world_size
 
         # Don't try to access the runner on AMD
         if self.aot_schedule:
@@ -794,15 +796,6 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
             )
         )
 
-        # Note(hc): update seq_lens of decode reqs under DCP.
-        if self.dcp_world_size > 1:
-            assert dcp_local_seq_lens is not None
-            dcp_local_seq_lens[:num_decodes] = seq_lens[
-                :num_decodes
-            ] // self.dcp_world_size + (
-                self.dcp_rank < seq_lens[:num_decodes] % self.dcp_world_size
-            )
-
         assert num_decodes + num_prefills == num_reqs
         assert num_decode_tokens + num_prefill_tokens == num_tokens
 
@@ -811,11 +804,6 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
             reqs_start = num_decodes  # prefill_start
 
             context_lens_cpu = num_computed_tokens_cpu[reqs_start:num_reqs]
-            # Note(hc): The context lengths in the perspective of dcp rank0.
-            cp_context_lens_cpu = torch.ceil(
-                context_lens_cpu.float() / self.dcp_world_size
-            ).int()
-            origin_context_lens = context_lens_cpu.tolist()
             max_context_len_cpu = context_lens_cpu.max().item()
             num_prefills_with_context_cpu = (context_lens_cpu > 0).sum().item()
             prefill_query_start_loc = (
@@ -871,32 +859,56 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                 )
 
                 if self.dcp_world_size > 1:
+                    local_context_lens_allranks = get_dcp_local_seq_lens(
+                        context_lens_cpu,
+                        self.dcp_world_size,
+                        None,
+                        self.dcp_local_block_size,
+                    )
+                    # Note(qcs): The max local context lengths
+                    # padded to `dcp_local_block_size`.
+                    padded_local_context_lens_cpu = (
+                        cdiv(
+                            context_lens_cpu,
+                            self.dcp_virtual_block_size,
+                        )
+                        * self.dcp_local_block_size
+                    )
                     # Note(hc): The above max_context_chunk already enforces
                     # block_size alignment, DCP just need the block_size can
                     # be divisible by dcp_world_size, because DCP use
                     # cp_gather_cache which not require `cp_chunk_starts`
                     # aligned to page_size.
                     assert max_context_chunk % self.dcp_world_size == 0
-                    cp_max_context_chunk = max_context_chunk // self.dcp_world_size
+                    padded_local_max_context_chunk_across_ranks = (
-                    cp_chunk_starts = (
+                        cdiv(
+                            max_context_chunk,
+                            self.dcp_virtual_block_size,
+                        )
+                        * self.dcp_local_block_size
+                    )
+                    local_chunk_starts = (
                         torch.arange(num_chunks, dtype=torch.int32)
                         .unsqueeze(1)
                         .expand(-1, num_prefills)
-                        * cp_max_context_chunk
+                        * padded_local_max_context_chunk_across_ranks
                     )
-                    cp_chunk_ends = torch.min(
+                    local_chunk_ends = torch.min(
-                        cp_context_lens_cpu.unsqueeze(0),
+                        padded_local_context_lens_cpu.unsqueeze(0),
-                        cp_chunk_starts + cp_max_context_chunk,
+                        local_chunk_starts
+                        + padded_local_max_context_chunk_across_ranks,
                     )
-                    cp_chunk_seq_lens = (cp_chunk_ends - cp_chunk_starts).clamp(min=0)
+                    padded_local_chunk_seq_lens = (
+                        local_chunk_ends - local_chunk_starts
+                    ).clamp(min=0)
 
-                    cp_cu_seq_lens_cpu = torch.zeros(
+                    padded_local_cu_chunk_seq_lens_cpu = torch.zeros(
                         num_chunks, num_prefills + 1, dtype=torch.int32, pin_memory=True
                     )
                     torch.cumsum(
-                        cp_chunk_seq_lens,
+                        padded_local_chunk_seq_lens,
                         dim=1,
-                        out=cp_cu_seq_lens_cpu[:, 1:],
+                        out=padded_local_cu_chunk_seq_lens_cpu[:, 1:],
                         dtype=torch.int32,
                     )
 
@@ -908,15 +920,16 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                 if self.dcp_world_size > 1:
                     chunked_context_metadata = chunked_context_metadata_cls(
                         cu_seq_lens=cu_seq_lens_cpu.to(device, non_blocking=True),
-                        starts=cp_chunk_starts.to(device, non_blocking=True),
+                        starts=local_chunk_starts.to(device, non_blocking=True),
-                        seq_tot=cp_chunk_seq_lens.sum(dim=1).tolist(),
+                        seq_tot=padded_local_chunk_seq_lens.sum(dim=1).tolist(),
                         max_seq_lens=chunk_seq_lens.max(dim=1).values.tolist(),
                         seq_lens=chunk_seq_lens,
                         workspace=self.chunked_prefill_workspace,
-                        cp_chunk_seq_lens=cp_chunk_seq_lens.tolist(),
+                        padded_local_chunk_seq_lens=padded_local_chunk_seq_lens.tolist(),
-                        origin_context_lens=origin_context_lens,
+                        local_context_lens_allranks=local_context_lens_allranks.tolist(),
-                        cp_cu_seq_lens=cp_cu_seq_lens_cpu.to(device, non_blocking=True),
+                        padded_local_cu_seq_lens=padded_local_cu_chunk_seq_lens_cpu.to(
-                        chunk_size=max_context_chunk,
+                            device, non_blocking=True
+                        ),
                         cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
                     )
                 else:
@@ -998,64 +1011,52 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
 def reorg_kvcache(
     allgatered_kv_c_normed: torch.Tensor,
     allgatered_k_pe: torch.Tensor,
-    cp_chunk_seq_lens_lst: list[int],
+    padded_local_chunk_seq_lens_lst: list[int],
-    origin_context_lens: list[int],
+    local_context_lens_allranks: list[list[int]],
-    cp_world_size: int,
     sum_seq_len: int,
     max_seq_len: int,
-    chunk_size: int,
-    chunk_idx: int,
     toks: int,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
-    reorg kvcache after cp local gather to tp layout for attn kernel.
+    reorg and unpad kvcache after cp local gather to tp layout for attn kernel.
-
+    e.g.
+    allgatered_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T1_0, T1_1, ...,
+                              T0_4, T0_5, pad, pad, T1_2, pad, ...]
+    -> reorganized_kv_c_normed = [T0_0, T0_1, T0_2, T0_3, T0_4, T0_5,
+                                  T1_0, T1_1, T1_2, ...]
     Args:
-        cp_chunk_seq_lens_lst: chunk context lengths under CP.
+        padded_local_chunk_seq_lens_lst: local chunk context lengths
-        origin_context_lens: origin full context lengths under CP.
+            under current CP rank.
-        cp_world_size: CP size.
+        local_context_lens_allranks: local context lengths on each CP rank.
         sum_seq_len: the sum of cp_chunk_seq_lens_lst.
         max_seq_len: the max value of cp_chunk_seq_lens_lst.
-        chunk_size: equals to max_context_chunk from
-            chunked_context_metadata building.
-        chunk_idx: chunk idx of chunked_prefill.
         toks: the number of tokens for local gather cache.
     """
     kv_c_segments = []
     k_pe_segments = []
     src_token_idx = 0
     max_seq_len_check = 0
-    for cp_chunk_seq_len, origin_context_len in zip(
+    for padded_local_chunk_seq_len, local_context_lens in zip(
-        cp_chunk_seq_lens_lst, origin_context_lens
+        padded_local_chunk_seq_lens_lst, local_context_lens_allranks
     ):
-        chunk_context_len = chunk_size
-        if cp_chunk_seq_len != 0:
-            chunk_context_len = min(
-                chunk_context_len, origin_context_len - chunk_size * chunk_idx
-            )
-        cp_target_rank = (chunk_context_len - 1) % cp_world_size
         cur_seq_len = 0
-        for rank in range(cp_world_size):
+        for rank, local_context_len in enumerate(local_context_lens):
-            if rank > cp_target_rank and cp_chunk_seq_len:
+            if local_context_len != 0:
-                real_cp_chunk_seq_len = cp_chunk_seq_len - 1
-            else:
-                real_cp_chunk_seq_len = cp_chunk_seq_len
-            if real_cp_chunk_seq_len:
                 kv_c_segment = allgatered_kv_c_normed[
                     rank * toks + src_token_idx : rank * toks
                     + src_token_idx
-                    + real_cp_chunk_seq_len
+                    + local_context_len
                 ]
                 k_pe_segment = allgatered_k_pe[
                     rank * toks + src_token_idx : rank * toks
                     + src_token_idx
-                    + real_cp_chunk_seq_len
+                    + local_context_len
                 ]
                 kv_c_segments.append(kv_c_segment)
                 k_pe_segments.append(k_pe_segment)
-                cur_seq_len += real_cp_chunk_seq_len
+                cur_seq_len += local_context_len
         max_seq_len_check = max(max_seq_len_check, cur_seq_len)
-        src_token_idx += cp_chunk_seq_len
+        src_token_idx += padded_local_chunk_seq_len
     reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
     reorganized_k_pe = torch.cat(k_pe_segments, dim=0)
     assert reorganized_kv_c_normed.shape[0] == sum_seq_len
@@ -1296,6 +1297,9 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
                 get_current_vllm_config()
             )
         )
+        self.dcp_kv_cache_interleave_size: int = (
+            get_current_vllm_config().parallel_config.dcp_kv_cache_interleave_size
+        )
 
     def _flash_attn_varlen_diff_headdims(
         self, q, k, v, return_softmax_lse=False, softmax_scale=None, **kwargs
@@ -1697,10 +1701,9 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
         assert attn_metadata.prefill is not None
         prefill_metadata = attn_metadata.prefill
         assert prefill_metadata.chunked_context is not None
-        assert prefill_metadata.chunked_context.cp_chunk_seq_lens is not None
+        assert prefill_metadata.chunked_context.padded_local_chunk_seq_lens is not None
-        assert prefill_metadata.chunked_context.origin_context_lens is not None
+        assert prefill_metadata.chunked_context.local_context_lens_allranks is not None
-        assert prefill_metadata.chunked_context.cp_cu_seq_lens is not None
+        assert prefill_metadata.chunked_context.padded_local_cu_seq_lens is not None
-        assert prefill_metadata.chunked_context.chunk_size is not None
         assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None
 
         output = None
@@ -1713,7 +1716,9 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
                 src_cache=kv_c_and_k_pe_cache,
                 dst=workspace,
                 block_table=prefill_metadata.block_table,
-                cu_seq_lens=prefill_metadata.chunked_context.cp_cu_seq_lens[i],
+                cu_seq_lens=prefill_metadata.chunked_context.padded_local_cu_seq_lens[
+                    i
+                ],
                 batch_size=attn_metadata.num_prefills,
                 seq_starts=prefill_metadata.chunked_context.starts[i],
             )
@@ -1743,15 +1748,12 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
             kv_c_normed, k_pe = reorg_kvcache(
                 allgatered_kv_c_normed,
                 allgatered_k_pe,
-                cp_chunk_seq_lens_lst=prefill_metadata.chunked_context.cp_chunk_seq_lens[
+                padded_local_chunk_seq_lens_lst=prefill_metadata.chunked_context.padded_local_chunk_seq_lens[
                     i
                 ],
-                origin_context_lens=prefill_metadata.chunked_context.origin_context_lens,
+                local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks,
-                cp_world_size=dcp_world_size,
                 sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1],
                 max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
-                chunk_size=prefill_metadata.chunked_context.chunk_size,
-                chunk_idx=i,
                 toks=toks,
             )
 

vllm/v1/attention/backends/utils.py

@@ -1076,3 +1076,41 @@ def compute_causal_conv1d_metadata(query_start_loc_p: torch.Tensor):
         nums_dict[BLOCK_M]["token_chunk_offset_ptr"] = token_chunk_offset_ptr  # type: ignore
 
     return nums_dict, batch_ptr, token_chunk_offset_ptr
+
+
+def get_dcp_local_seq_lens(
+    seq_lens: torch.Tensor,
+    dcp_world_size: int = 1,
+    dcp_rank: int | None = None,
+    dcp_kv_cache_interleave_size: int = 1,
+) -> torch.Tensor:
+    """While using dcp, kv_cache size stored on each rank may be different,
+    use this function to calculate split decode seq_lens of each dcp rank.
+    Only consider dcp now, we can extend the case of cp based on this.
+    """
+    num_requests = seq_lens.size(0)
+    if dcp_rank is None:
+        rank_offsets = (
+            torch.arange(dcp_world_size, dtype=torch.int32)
+            .unsqueeze(0)
+            .repeat(num_requests, 1)
+        )
+    else:
+        rank_offsets = torch.Tensor([[dcp_rank]]).to(dtype=torch.int32)
+    seq_lens_tiled = (
+        seq_lens.to(torch.int32).unsqueeze(-1).repeat(1, rank_offsets.shape[1])
+    )
+    base = (
+        seq_lens_tiled
+        // dcp_kv_cache_interleave_size
+        // dcp_world_size
+        * dcp_kv_cache_interleave_size
+    )
+    remainder = seq_lens_tiled - base * dcp_world_size
+    remainder = torch.clip(
+        remainder - rank_offsets * dcp_kv_cache_interleave_size,
+        0,
+        dcp_kv_cache_interleave_size,
+    )
+    dcp_local_seq_lens = base + remainder
+    return dcp_local_seq_lens.squeeze(1)

vllm/v1/worker/block_table.py

@@ -22,6 +22,7 @@ class BlockTable:
         pin_memory: bool,
         device: torch.device,
         kernel_block_size: int,
+        dcp_kv_cache_interleave_size: int,
     ):
         """
         Args:
@@ -86,6 +87,7 @@ class BlockTable:
             # DCP might not be initialized in testing
             self.dcp_world_size = 1
             self.dcp_rank = 0
+        self.dcp_kv_cache_interleave_size = dcp_kv_cache_interleave_size
 
     def append_row(
         self,
@@ -144,9 +146,19 @@ class BlockTable:
             # Use virtual_block_size for mask calculation, which marks local
             # tokens.
             virtual_block_offsets = positions % virtual_block_size
-            mask = virtual_block_offsets % self.dcp_world_size == self.dcp_rank
+            mask = (
+                virtual_block_offsets
+                // self.dcp_kv_cache_interleave_size
+                % self.dcp_world_size
+                == self.dcp_rank
+            )
             # Calculate local block_offsets
-            block_offsets = virtual_block_offsets // self.dcp_world_size
+            block_offsets = (
+                virtual_block_offsets
+                // (self.dcp_world_size * self.dcp_kv_cache_interleave_size)
+                * self.dcp_kv_cache_interleave_size
+                + virtual_block_offsets % self.dcp_kv_cache_interleave_size
+            )
             # Calculate slot_mapping
             slot_mapping = block_numbers * self.block_size + block_offsets
             # Write final slots, use -1 for not-local
@@ -234,6 +246,7 @@ class MultiGroupBlockTable:
         block_sizes: list[int],
         kernel_block_sizes: list[int],
         num_speculative_tokens: int = 0,
+        dcp_kv_cache_interleave_size: int = 1,
     ) -> None:
         # Note(hc): each dcp rank only store
         # (max_model_len//dcp_world_size) tokens in kvcache,
@@ -263,6 +276,7 @@ class MultiGroupBlockTable:
                 pin_memory,
                 device,
                 kernel_block_size,
+                dcp_kv_cache_interleave_size,
             )
             for block_size, kernel_block_size in zip(block_sizes, kernel_block_sizes)
         ]

vllm/v1/worker/gpu_input_batch.py

@@ -84,6 +84,7 @@ class InputBatch:
         is_spec_decode: bool = False,
         is_pooling_model: bool = False,
         num_speculative_tokens: int = 0,
+        dcp_kv_cache_interleave_size: int = 1,
     ):
         self.is_pooling_model = is_pooling_model
         self.is_spec_decode = is_spec_decode
@@ -137,6 +138,7 @@ class InputBatch:
             block_sizes=block_sizes,
             kernel_block_sizes=kernel_block_sizes,
             num_speculative_tokens=num_speculative_tokens,
+            dcp_kv_cache_interleave_size=dcp_kv_cache_interleave_size,
         )
 
         # Sampling-related.

vllm/v1/worker/gpu_model_runner.py

@@ -35,6 +35,7 @@ from vllm.distributed.eplb.eplb_state import EplbState
 from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
 from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
 from vllm.distributed.parallel_state import (
+    get_dcp_group,
     get_pp_group,
     get_tp_group,
     graph_capture,
@@ -88,6 +89,7 @@ from vllm.v1.attention.backends.utils import (
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
     create_fast_prefill_custom_backend,
+    get_dcp_local_seq_lens,
     reorder_batch_to_split_decodes_and_prefills,
     split_attn_metadata,
 )
@@ -275,6 +277,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         self.is_multimodal_pruning_enabled = False
         self.max_model_len = model_config.max_model_len
         self.dcp_world_size = self.parallel_config.decode_context_parallel_size
+        self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
         self.max_num_tokens = scheduler_config.max_num_batched_tokens
         self.max_num_reqs = scheduler_config.max_num_seqs
 
@@ -396,6 +399,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             # uses output token ids so we set this conservatively.
             logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
             is_pooling_model=self.is_pooling_model,
+            dcp_kv_cache_interleave_size=self.parallel_config.dcp_kv_cache_interleave_size,
         )
 
         self.use_async_scheduling = self.scheduler_config.async_scheduling
@@ -1307,6 +1311,16 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 logits_indices
             )
 
+        # update seq_lens of decode reqs under DCP.
+        if self.dcp_world_size > 1:
+            self.dcp_local_seq_lens.cpu[:num_reqs] = get_dcp_local_seq_lens(
+                self.seq_lens.cpu[:num_reqs],
+                self.dcp_world_size,
+                self.dcp_rank,
+                self.parallel_config.dcp_kv_cache_interleave_size,
+            )
+            self.dcp_local_seq_lens.copy_to_gpu(num_reqs)
+
         attn_metadata: PerLayerAttnMetadata = {}
         if ubatch_slices is not None:
             attn_metadata = [dict() for _ in range(len(ubatch_slices))]