support kv_cache interleave_size and pd disaggregate

Signed-off-by: zhangsicheng5 <zhangsicheng5@huawei.com>

Author: zhangsicheng5

tests/distributed/test_context_parallel.py

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

vllm/config/parallel.py

@@ -223,6 +223,17 @@ class is dynamically inherited by the worker class. This is used to inject
     not change by dcp, it simply reuse the GPUs of TP group, and tp_size
     needs to be divisible by dcp_size."""
 
+    cp_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 cp_kv_cache_interleave_size.
+    Block_size should be divisible by cp_kv_cache_interleave_size.
+    """
+
     _api_process_count: int = Field(default=1, gt=0)
     """
     The number of API processes initialized.

vllm/config/vllm.py

@@ -480,6 +480,23 @@ def __post_init__(self):
             )
         current_platform.check_and_update_config(self)
 
+        assert (
+            self.parallel_config.cp_kv_cache_interleave_size
+            <= self.cache_config.block_size
+            and self.cache_config.block_size
+            % self.parallel_config.cp_kv_cache_interleave_size
+            == 0
+        ), (
+            f"Block_size({self.cache_config.block_size}) should be "
+            "greater than or equal to and divisible by cp_kv_cache_interleave_size "
+            f"({self.parallel_config.cp_kv_cache_interleave_size})."
+        )
+
+        assert (
+            self.parallel_config.cp_kv_cache_interleave_size == 1
+            or self.speculative_config is None
+        ), "MTP with cp_kv_cache_interleave_size > 1 is not supported now."
+
         # Do this after all the updates to compilation_config.mode
         if (
             envs.VLLM_USE_V1

vllm/engine/arg_utils.py

@@ -362,6 +362,7 @@ 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
+    cp_kv_cache_interleave_size: int = ParallelConfig.cp_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
@@ -717,6 +718,10 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             "-dcp",
             **parallel_kwargs["decode_context_parallel_size"],
         )
+        parallel_group.add_argument(
+            "--cp-kv-cache-interleave-size",
+            **parallel_kwargs["cp_kv_cache_interleave_size"],
+        )
         parallel_group.add_argument(
             "--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]
         )
@@ -1482,6 +1487,7 @@ def create_engine_config(
             worker_cls=self.worker_cls,
             worker_extension_cls=self.worker_extension_cls,
             decode_context_parallel_size=self.decode_context_parallel_size,
+            cp_kv_cache_interleave_size=self.cp_kv_cache_interleave_size,
             _api_process_count=self._api_process_count,
             _api_process_rank=self._api_process_rank,
         )

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

@@ -774,15 +774,6 @@ def build(
             )
         )
 
-        # 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] - 1) % self.dcp_world_size
-            )
-
         assert num_decodes + num_prefills == num_reqs
         assert num_decode_tokens + num_prefill_tokens == num_tokens
 

vllm/v1/attention/backends/utils.py

@@ -992,3 +992,35 @@ 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,
+    cp_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)
+    seq_lens_tiled = seq_lens.unsqueeze(-1).repeat(1, dcp_world_size)
+    rank_offsets = (
+        torch.arange(dcp_world_size, dtype=torch.int32)
+        .unsqueeze(0)
+        .repeat(num_requests, 1)
+    )
+    base = (
+        seq_lens_tiled
+        // cp_kv_cache_interleave_size
+        // dcp_world_size
+        * cp_kv_cache_interleave_size
+    )
+    remainder = seq_lens_tiled - base * dcp_world_size
+    remainder = torch.clip(
+        remainder - rank_offsets * cp_kv_cache_interleave_size,
+        0,
+        cp_kv_cache_interleave_size,
+    )
+    dcp_local_seq_lens = base + remainder
+    return dcp_local_seq_lens

vllm/v1/worker/block_table.py

@@ -22,6 +22,7 @@ def __init__(
         pin_memory: bool,
         device: torch.device,
         kernel_block_size: int,
+        cp_kv_cache_interleave_size: int,
     ):
         """
         Args:
@@ -86,6 +87,7 @@ def __init__(
             # DCP might not be initialized in testing
             self.dcp_world_size = 1
             self.dcp_rank = 0
+        self.cp_kv_cache_interleave_size = cp_kv_cache_interleave_size
 
     def append_row(
         self,
@@ -144,15 +146,26 @@ def compute_slot_mapping(
             # 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.cp_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.cp_kv_cache_interleave_size)
+                * self.cp_kv_cache_interleave_size
+                + virtual_block_offsets % self.cp_kv_cache_interleave_size
+            )
             # Calculate slot_mapping
             slot_mapping = block_numbers * self.block_size + block_offsets
             # Write final slots, use -1 for not-local
             self.slot_mapping.np[: req_indices.shape[0]] = np.where(
                 mask, slot_mapping, -1
             )
+            logger.info(f'>>>>> rank={torch.distributed.get_rank()}, slot_mapping={self.slot_mapping.np[: req_indices.shape[0]]}')
         else:
             block_table_indices = (
                 req_indices * self.max_num_blocks_per_req + positions // self.block_size
@@ -234,6 +247,7 @@ def __init__(
         block_sizes: list[int],
         kernel_block_sizes: list[int],
         num_speculative_tokens: int = 0,
+        cp_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 +277,7 @@ def __init__(
                 pin_memory,
                 device,
                 kernel_block_size,
+                cp_kv_cache_interleave_size,
             )
             for block_size, kernel_block_size in zip(block_sizes, kernel_block_sizes)
         ]

vllm/v1/worker/gpu_input_batch.py

@@ -83,6 +83,7 @@ def __init__(
         is_spec_decode: bool = False,
         is_pooling_model: bool = False,
         num_speculative_tokens: int = 0,
+        cp_kv_cache_interleave_size: int = 1,
     ):
         self.is_pooling_model = is_pooling_model
         self.is_spec_decode = is_spec_decode
@@ -135,6 +136,7 @@ def __init__(
             block_sizes=block_sizes,
             kernel_block_sizes=kernel_block_sizes,
             num_speculative_tokens=num_speculative_tokens,
+            cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
         )
 
         # Sampling-related.

vllm/v1/worker/gpu_model_runner.py

@@ -35,6 +35,7 @@
 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,
@@ -92,6 +93,7 @@
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
     create_fast_prefill_custom_backend,
+    get_dcp_local_seq_lens,
     reorder_batch_to_split_decodes_and_prefills,
     split_attn_metadata,
 )
@@ -256,6 +258,11 @@ def __init__(
         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
+        try:
+            self.dcp_rank = get_dcp_group().rank_in_group
+        except AssertionError:
+            # DCP might not be initialized in testing
+            self.dcp_rank = 0
         self.max_num_tokens = scheduler_config.max_num_batched_tokens
         self.max_num_reqs = scheduler_config.max_num_seqs
 
@@ -372,6 +379,7 @@ def __init__(
             # uses output token ids so we set this conservatively.
             logitsprocs_need_output_token_ids=bool(custom_logitsprocs),
             is_pooling_model=self.is_pooling_model,
+            cp_kv_cache_interleave_size=self.parallel_config.cp_kv_cache_interleave_size,
         )
 
         self.use_async_scheduling = self.scheduler_config.async_scheduling
@@ -1274,6 +1282,15 @@ def _prepare_inputs(
                 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.parallel_config.cp_kv_cache_interleave_size,
+            )[:, self.dcp_rank]
+            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))]