[Feature] Decode Context Parallel support for GPU model runner v2 (#34179)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/v1/worker/gpu/attn_utils.py

@@ -12,6 +12,7 @@ from vllm.v1.attention.backend import (
     AttentionMetadataBuilder,
     CommonAttentionMetadata,
 )
+from vllm.v1.attention.backends.utils import get_dcp_local_seq_lens
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
     KVCacheConfig,
@@ -143,6 +144,28 @@ def build_slot_mappings_by_layer(
     return slot_mappings_by_layer
 
 
+def prepare_dcp_local_seq_lens(
+    dcp_local_seq_lens: torch.Tensor,
+    seq_lens: torch.Tensor,
+    num_reqs: int,
+    dcp_size: int,
+    dcp_rank: int,
+    cp_kv_cache_interleave_size: int,
+) -> None:
+    """Populate the persistent DCP local seq_lens buffer (CUDA graph safe)."""
+    if dcp_size <= 1:
+        return
+
+    local_seq_lens = get_dcp_local_seq_lens(
+        seq_lens[:num_reqs],
+        dcp_size=dcp_size,
+        dcp_rank=dcp_rank,
+        cp_kv_cache_interleave_size=cp_kv_cache_interleave_size,
+    )
+    dcp_local_seq_lens[:num_reqs].copy_(local_seq_lens, non_blocking=True)
+    dcp_local_seq_lens[num_reqs:].zero_()
+
+
 def build_attn_metadata(
     attn_metadata_builders: list[AttentionMetadataBuilder],
     num_reqs: int,
@@ -155,9 +178,13 @@ def build_attn_metadata(
     block_tables: Sequence[torch.Tensor],
     slot_mappings: torch.Tensor,
     kv_cache_config: KVCacheConfig,
+    dcp_local_seq_lens: torch.Tensor | None = None,
 ) -> dict[str, Any]:
     seq_lens = seq_lens[:num_reqs]
 
+    if dcp_local_seq_lens is not None:
+        dcp_local_seq_lens = dcp_local_seq_lens[:num_reqs]
+
     attn_metadata: dict[str, Any] = {}
     kv_cache_groups = kv_cache_config.kv_cache_groups
     for i, kv_cache_spec in enumerate(kv_cache_groups):
@@ -175,6 +202,7 @@ def build_attn_metadata(
             block_table_tensor=block_table,
             slot_mapping=slot_mapping,
             causal=True,
+            dcp_local_seq_lens=dcp_local_seq_lens,
         )
 
         attn_metadata_builder = attn_metadata_builders[i]

vllm/v1/worker/gpu/block_table.py

@@ -4,6 +4,7 @@ from collections.abc import Iterable
 
 import torch
 
+from vllm.distributed import get_dcp_group
 from vllm.triton_utils import tl, triton
 from vllm.utils.math_utils import cdiv
 from vllm.v1.attention.backends.utils import PAD_SLOT_ID
@@ -18,19 +19,36 @@ class BlockTables:
         max_num_batched_tokens: int,
         max_model_len: int,
         device: torch.device,
+        cp_kv_cache_interleave_size: int = 1,
     ):
         self.block_sizes = block_sizes
         self.max_num_reqs = max_num_reqs
         self.max_num_batched_tokens = max_num_batched_tokens
         self.max_model_len = max_model_len
         self.device = device
+        assert cp_kv_cache_interleave_size >= 1
+        self.cp_kv_cache_interleave_size = cp_kv_cache_interleave_size
+
+        try:
+            dcp = get_dcp_group()
+            self.dcp_world_size, self.dcp_rank = dcp.world_size, dcp.rank_in_group
+        except AssertionError:
+            self.dcp_world_size, self.dcp_rank = 1, 0
+        # TODO(wentao): PCP supprot
+        self.total_cp_world_size = self.dcp_world_size
+        self.total_cp_rank = self.dcp_rank
 
         self.num_kv_cache_groups = len(self.block_sizes)
         # num_kv_cache_groups x [max_num_reqs, max_num_blocks]
         self.block_tables: list[StagedWriteTensor] = []
         for i in range(self.num_kv_cache_groups):
             block_size = self.block_sizes[i]
-            max_num_blocks = cdiv(self.max_model_len, block_size)
+            # with DCP, a request's KV is sharded across
+            # ranks, so one physical block on this rank
+            # corresponds to `block_size * total_cp_world_size`
+            # tokens in the global (unsharded) sequence.
+            virtual_block_size = block_size * self.total_cp_world_size
+            max_num_blocks = cdiv(self.max_model_len, virtual_block_size)
             block_table = StagedWriteTensor(
                 (self.max_num_reqs, max_num_blocks),
                 dtype=torch.int32,
@@ -131,6 +149,9 @@ class BlockTables:
             self.block_sizes_tensor,
             self.slot_mappings,
             self.slot_mappings.stride(0),
+            TOTAL_CP_WORLD_SIZE=self.total_cp_world_size,
+            TOTAL_CP_RANK=self.total_cp_rank,
+            CP_KV_CACHE_INTERLEAVE_SIZE=self.cp_kv_cache_interleave_size,
             PAD_ID=PAD_SLOT_ID,
             TRITON_BLOCK_SIZE=1024,  # type: ignore
         )
@@ -183,6 +204,9 @@ def _compute_slot_mappings_kernel(
     block_sizes,  # [num_kv_cache_groups]
     slot_mappings_ptr,  # [num_kv_cache_groups, max_num_tokens]
     slot_mappings_stride,
+    TOTAL_CP_WORLD_SIZE: tl.constexpr,
+    TOTAL_CP_RANK: tl.constexpr,
+    CP_KV_CACHE_INTERLEAVE_SIZE: tl.constexpr,
     PAD_ID: tl.constexpr,
     TRITON_BLOCK_SIZE: tl.constexpr,
 ):
@@ -201,6 +225,7 @@ def _compute_slot_mappings_kernel(
     block_table_ptr = _load_ptr(block_table_ptrs + group_id, tl.int32)
     block_table_stride = tl.load(block_table_strides + group_id)
     block_size = tl.load(block_sizes + group_id)
+    virtual_block_size = block_size * TOTAL_CP_WORLD_SIZE
 
     req_state_idx = tl.load(idx_mapping + batch_idx)
     start_idx = tl.load(query_start_loc + batch_idx)
@@ -208,11 +233,26 @@ def _compute_slot_mappings_kernel(
     for i in range(start_idx, end_idx, TRITON_BLOCK_SIZE):
         offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
         positions = tl.load(pos + offset, mask=offset < end_idx, other=0)
-        block_indices = positions // block_size
+        block_indices = positions // virtual_block_size
         block_numbers = tl.load(
             block_table_ptr + req_state_idx * block_table_stride + block_indices
         )
-        slot_ids = block_numbers * block_size + positions % block_size
+        virtual_block_offsets = positions - block_indices * virtual_block_size
+
+        # determine whether the token is stored on this CP rank.
+        is_local = (
+            virtual_block_offsets // CP_KV_CACHE_INTERLEAVE_SIZE
+        ) % TOTAL_CP_WORLD_SIZE == TOTAL_CP_RANK
+        # mapping virture block offsets to local block offsets.
+        local_block_offsets = (
+            virtual_block_offsets // (TOTAL_CP_WORLD_SIZE * CP_KV_CACHE_INTERLEAVE_SIZE)
+        ) * CP_KV_CACHE_INTERLEAVE_SIZE + (
+            virtual_block_offsets % CP_KV_CACHE_INTERLEAVE_SIZE
+        )
+
+        # physical slot index
+        slot_ids = block_numbers * block_size + local_block_offsets
+        slot_ids = tl.where(is_local, slot_ids, PAD_ID)
         tl.store(slot_mapping_ptr + offset, slot_ids, mask=offset < end_idx)
 
 

vllm/v1/worker/gpu/cudagraph_utils.py

@@ -10,6 +10,7 @@ from tqdm import tqdm
 
 from vllm.config import VllmConfig
 from vllm.config.compilation import CUDAGraphMode
+from vllm.distributed import get_dcp_group
 from vllm.distributed.parallel_state import graph_capture, is_global_first_rank
 from vllm.forward_context import set_forward_context
 from vllm.v1.attention.backend import AttentionMetadataBuilder
@@ -17,6 +18,7 @@ from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.worker.gpu.attn_utils import (
     build_attn_metadata,
     build_slot_mappings_by_layer,
+    prepare_dcp_local_seq_lens,
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.dp_utils import make_num_tokens_across_dp
@@ -257,6 +259,23 @@ def prepare_inputs_to_capture(
     input_buffers.seq_lens[:num_reqs] = num_tokens
     input_buffers.seq_lens[num_reqs:] = 0
 
+    try:
+        dcp_group = get_dcp_group()
+        dcp_world_size = dcp_group.world_size
+        dcp_rank = dcp_group.rank_in_group
+    except AssertionError:
+        dcp_world_size = 1
+        dcp_rank = 0
+    if dcp_world_size > 1:
+        prepare_dcp_local_seq_lens(
+            input_buffers.dcp_local_seq_lens,
+            input_buffers.seq_lens,
+            num_reqs,
+            dcp_size=dcp_world_size,
+            dcp_rank=dcp_rank,
+            cp_kv_cache_interleave_size=block_tables.cp_kv_cache_interleave_size,
+        )
+
     input_block_tables = [x[:num_reqs] for x in block_tables.input_block_tables]
     slot_mappings = block_tables.slot_mappings[:, :num_tokens]
     slot_mappings_by_layer = build_slot_mappings_by_layer(
@@ -275,5 +294,6 @@ def prepare_inputs_to_capture(
         block_tables=input_block_tables,
         slot_mappings=slot_mappings,
         kv_cache_config=kv_cache_config,
+        dcp_local_seq_lens=input_buffers.dcp_local_seq_lens,
     )
     return attn_metadata, slot_mappings_by_layer

vllm/v1/worker/gpu/input_batch.py

@@ -27,6 +27,10 @@ class InputBuffers:
             max_num_reqs + 1, dtype=torch.int32, device=device
         )
         self.seq_lens = torch.zeros(max_num_reqs, dtype=torch.int32, device=device)
+        # DCP: per-request local seq_lens buffer
+        self.dcp_local_seq_lens = torch.zeros(
+            max_num_reqs, dtype=torch.int32, device=device
+        )
 
 
 @dataclass

vllm/v1/worker/gpu/model_runner.py

@@ -11,6 +11,7 @@ import torch.nn as nn
 from vllm.config import VllmConfig
 from vllm.config.compilation import CUDAGraphMode
 from vllm.distributed.parallel_state import (
+    get_dcp_group,
     get_pp_group,
     prepare_communication_buffer_for_model,
 )
@@ -24,6 +25,7 @@ from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
 from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
+from vllm.v1.worker.cp_utils import check_attention_cp_compatibility
 from vllm.v1.worker.gpu.async_utils import AsyncOutput
 from vllm.v1.worker.gpu.attn_utils import (
     build_attn_metadata,
@@ -31,6 +33,7 @@ from vllm.v1.worker.gpu.attn_utils import (
     get_kv_cache_spec,
     init_attn_backend,
     init_kv_cache,
+    prepare_dcp_local_seq_lens,
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.buffer_utils import async_copy_to_gpu
@@ -248,11 +251,15 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             max_num_batched_tokens=self.max_num_tokens,
             max_model_len=self.max_model_len,
             device=self.device,
+            cp_kv_cache_interleave_size=(
+                self.parallel_config.cp_kv_cache_interleave_size
+            ),
         )
 
         self.attn_backends, self.attn_metadata_builders = init_attn_backend(
             self.kv_cache_config, self.vllm_config, self.device
         )
+        check_attention_cp_compatibility(self.vllm_config)
         if self.do_spec_decode:
             # HACK(woosuk)
             self.speculator.set_attn(
@@ -294,6 +301,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             block_tables=block_tables,
             slot_mappings=slot_mappings,
             kv_cache_config=self.kv_cache_config,
+            dcp_local_seq_lens=self.input_buffers.dcp_local_seq_lens,
         )
         input_batch.attn_metadata = attn_metadata
         input_batch.slot_mappings = slot_mappings_by_layer
@@ -627,6 +635,19 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         )
         seq_lens = self.input_buffers.seq_lens[:num_reqs]
 
+        dcp_size = self.parallel_config.decode_context_parallel_size
+        if dcp_size > 1:
+            prepare_dcp_local_seq_lens(
+                self.input_buffers.dcp_local_seq_lens,
+                seq_lens,
+                num_reqs,
+                dcp_size=dcp_size,
+                dcp_rank=get_dcp_group().rank_in_group,
+                cp_kv_cache_interleave_size=(
+                    self.parallel_config.cp_kv_cache_interleave_size
+                ),
+            )
+
         # Prepare M-RoPE positions.
         if self.uses_mrope:
             self.mrope_states.prepare_mrope_positions(
@@ -674,6 +695,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             block_tables=block_tables,
             slot_mappings=slot_mappings,
             kv_cache_config=self.kv_cache_config,
+            dcp_local_seq_lens=self.input_buffers.dcp_local_seq_lens,
         )
 
         input_ids = self.input_buffers.input_ids[:num_tokens_after_padding]