[Attention][DCP] Support DCP with query length > 1 (MTP) with FA3

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 4695e6bed5366c41e28c06cd86170166e4f43d00
+          GIT_TAG 8f468e7da54a8e2f98abfa7c38636aac91c0cba1
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

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

@@ -370,6 +370,7 @@ class ChunkedContextMetadata(MLACommonPrefillMetadata.ChunkedContextMetadata):
 class MLACommonDecodeMetadata:
     block_table: torch.Tensor
     seq_lens: torch.Tensor
+    dcp_tot_seq_lens: Optional[torch.Tensor]
 
 
 D = TypeVar("D", bound=MLACommonDecodeMetadata)
@@ -682,10 +683,12 @@ def _build_decode(
         query_start_loc_cpu: torch.Tensor,
         query_start_loc_device: torch.Tensor,
         num_decode_tokens: int,
+        dcp_tot_seq_lens_device: Optional[torch.Tensor],
     ) -> MLACommonDecodeMetadata:
         return MLACommonDecodeMetadata(
             block_table=block_table_tensor,
             seq_lens=seq_lens_device,
+            dcp_tot_seq_lens=dcp_tot_seq_lens_device,
         )
 
     def build_for_cudagraph_capture(
@@ -727,6 +730,7 @@ def build(
         query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
         seq_lens = common_attn_metadata.seq_lens
         seq_lens_cpu = common_attn_metadata.seq_lens_cpu
+        dcp_local_seq_lens = common_attn_metadata.dcp_local_seq_lens
 
         query_seq_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
 
@@ -742,7 +746,10 @@ def build(
 
         # Note(hc): update seq_lens of decode reqs under DCP.
         if self.dcp_world_size > 1:
-            seq_lens[:num_decodes] = seq_lens[:num_decodes] // self.dcp_world_size + (
+            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
             )
 
@@ -899,10 +906,15 @@ def build(
             decode_metadata = self._build_decode(
                 block_table_tensor=block_table_tensor[:num_decodes, ...],
                 seq_lens_cpu=seq_lens_cpu[:num_decodes],
-                seq_lens_device=seq_lens[:num_decodes],
+                seq_lens_device=dcp_local_seq_lens[:num_decodes]
+                if self.dcp_world_size > 1 and dcp_local_seq_lens is not None
+                else seq_lens[:num_decodes],
                 query_start_loc_cpu=query_start_loc_cpu[: num_decodes + 1],
                 query_start_loc_device=query_start_loc[: num_decodes + 1],
                 num_decode_tokens=num_decode_tokens,
+                dcp_tot_seq_lens_device=seq_lens[:num_decodes]
+                if self.dcp_world_size > 1
+                else None,
             )
 
         attn_metadata = self.metadata_cls(

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

@@ -17,7 +17,6 @@
     get_flash_attn_version,
 )
 from vllm.config import VllmConfig
-from vllm.distributed.parallel_state import get_dcp_group
 from vllm.logger import init_logger
 from vllm.v1.attention.backends.mla.common import (
     MLACommonBackend,
@@ -107,12 +106,6 @@ def __init__(
             # pre-allocated during capture.
             self.max_num_splits = envs.VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH
 
-        # TODO(lucas): Until we add support for the DCP custom masking we need
-        #   to restrict decodes to q_len == 1 when DCP is enabled.
-        self.reorder_batch_threshold = (
-            1 if get_dcp_group().world_size > 1 else self.reorder_batch_threshold
-        )
-
     def _schedule_decode(
         self, num_reqs, cu_query_lens, max_query_len, seqlens, max_seq_len, causal
     ):
@@ -121,7 +114,7 @@ def _schedule_decode(
                 batch_size=num_reqs,
                 max_seqlen_q=max_query_len,
                 max_seqlen_k=max_seq_len,
-                num_heads_q=self.num_heads,
+                num_heads_q=self.num_heads * self.dcp_world_size,
                 num_heads_kv=1,
                 headdim=self.mla_dims.qk_rope_head_dim,
                 cache_seqlens=seqlens,
@@ -142,10 +135,11 @@ def _build_decode(
         query_start_loc_cpu: torch.Tensor,
         query_start_loc_device: torch.Tensor,
         num_decode_tokens: int,
+        dcp_tot_seq_lens_device: Optional[torch.Tensor],
     ) -> FlashAttnMLADecodeMetadata:
         query_lens_cpu = query_start_loc_cpu[1:] - query_start_loc_cpu[:-1]
         max_query_len = query_lens_cpu.max().item()
-        max_seq_len = seq_lens_cpu.max().item()
+        max_seq_len = seq_lens_device.max().item()
 
         scheduler_metadata = self._schedule_decode(
             num_reqs=seq_lens_cpu.numel(),
@@ -188,6 +182,7 @@ def _build_decode(
             max_seq_len=max_seq_len,
             scheduler_metadata=scheduler_metadata,
             max_num_splits=max_num_splits,
+            dcp_tot_seq_lens=dcp_tot_seq_lens_device,
         )
 
 
@@ -289,6 +284,9 @@ def _forward_decode(
             fa_version=3,  # only version 3 is supported
             scheduler_metadata=attn_metadata.decode.scheduler_metadata,
             num_splits=attn_metadata.decode.max_num_splits,
+            cp_world_size=self.dcp_world_size,
+            cp_rank=self.dcp_rank,
+            cp_tot_seqused_k=attn_metadata.decode.dcp_tot_seq_lens,
         )
 
         if self.need_to_return_lse_for_decode:

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

@@ -106,6 +106,7 @@ def _build_decode(
         query_start_loc_cpu: torch.Tensor,
         query_start_loc_device: torch.Tensor,
         num_decode_tokens: int,
+        dcp_tot_seq_lens_device: Optional[torch.Tensor],
     ) -> FlashMLADecodeMetadata:
         tile_scheduler_metadata, num_splits = get_mla_metadata(
             seq_lens_device,
@@ -146,6 +147,7 @@ def _build_decode(
             seq_lens=seq_lens_device,
             tile_scheduler_metadata=tile_scheduler_metadata,
             num_splits=num_splits,
+            dcp_tot_seq_lens=dcp_tot_seq_lens_device,
         )
 
 

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

@@ -116,6 +116,7 @@ def _build_decode(
         query_start_loc_cpu: torch.Tensor,
         query_start_loc_device: torch.Tensor,
         num_decode_tokens: int,
+        dcp_tot_seq_lens_device: Optional[torch.Tensor],
     ) -> AiterMLADecodeMetadata:
         page_size = self.kv_cache_spec.block_size
         block_table_bounds = (seq_lens_device + page_size - 1) // page_size
@@ -174,6 +175,7 @@ def _build_decode(
             paged_kv_indices=paged_kv_indices,
             paged_kv_last_page_len=paged_kv_last_page_len,
             qo_indptr=qo_indptr,
+            dcp_tot_seq_lens=dcp_tot_seq_lens_device,
         )
 
         return attn_metadata

vllm/v1/attention/backends/utils.py

@@ -93,6 +93,9 @@ class CommonAttentionMetadata:
     # Needed by CrossAttentionBuilder
     encoder_seq_lens: Optional[np.ndarray] = None
 
+    dcp_local_seq_lens: Optional[torch.Tensor] = None
+    """Sequence lengths of the local rank in decode context parallelism world"""
+
 
 def slice_query_start_locs(
     query_start_loc: torch.Tensor,

vllm/v1/spec_decode/eagle.py

@@ -597,6 +597,7 @@ def prepare_inputs_padded(
             block_table_tensor=common_attn_metadata.block_table_tensor,
             slot_mapping=common_attn_metadata.slot_mapping[token_indices],
             causal=True,
+            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
         )
 
         token_indices_to_sample = (
@@ -868,6 +869,7 @@ def prepare_inputs(
             block_table_tensor=common_attn_metadata.block_table_tensor,
             slot_mapping=common_attn_metadata.slot_mapping[token_indices],
             causal=True,
+            dcp_local_seq_lens=common_attn_metadata.dcp_local_seq_lens,
         )
 
         return spec_common_attn_metadata, token_indices

vllm/v1/worker/gpu_model_runner.py

@@ -398,6 +398,10 @@ def __init__(
             self.max_num_reqs + 1, dtype=torch.int32
         )
         self.seq_lens = self._make_buffer(self.max_num_reqs, dtype=torch.int32)
+        if self.dcp_world_size > 1:
+            self.dcp_local_seq_lens = self._make_buffer(
+                self.max_num_reqs, dtype=torch.int32
+            )
         # Because inputs_embeds may be bfloat16 and we don't need a numpy
         # version of this tensor, avoid a RuntimeError by not creating a
         # numpy buffer.
@@ -581,7 +585,10 @@ def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
             # NOTE(lucas): currently no backend supports the custom masking
             #  required for DCP with q_len > 1, so we assert here. Remove this
             #  assert once the custom mask is support is added to FA3.
-            if self.dcp_world_size > 1:
+            if (
+                self.dcp_world_size > 1
+                and envs.VLLM_ATTENTION_BACKEND != "FLASH_ATTN_MLA"
+            ):
                 assert self.reorder_batch_threshold == 1, (
                     "DCP not support reorder_batch_threshold > 1 now."
                 )
@@ -1335,6 +1342,9 @@ def _prepare_inputs(
                 num_logits_indices=logits_indices.size(0),
                 causal=True,
                 encoder_seq_lens=encoder_seq_lens,
+                dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
+                if self.dcp_world_size > 1
+                else None,
             )
 
             if self.speculative_config and spec_decode_common_attn_metadata is None:
@@ -3309,6 +3319,9 @@ def _dummy_run(
                         kv_cache_group_id
                     ].slot_mapping.gpu[:num_tokens],
                     causal=True,
+                    dcp_local_seq_lens=self.dcp_local_seq_lens.gpu[:num_reqs]
+                    if self.dcp_world_size > 1
+                    else None,
                 )
                 for attn_group in self.attn_groups[kv_cache_group_id]:
                     if ubatch_slices is not None: