DCP supports hybrid attention by Yancey0623 · Pull Request #40996 · vllm-project/vllm

Yancey0623 · 2026-04-27T09:37:59Z

Purpose

Add DCP support for hybrid-attention models. Hybrid-attention models such as Qwen/Qwen3.5-0.8B contain both DCP-capable full-attention layers and non-DCP layers. This PR enables DCP for supported
attention groups without globally blocking hybrid-attention DCP, while keeping
non-DCP groups on local cache/state handling.

Test Plan

Run focused hybrid-attention DCP validation with
tests/distributed/test_context_parallel.py using model Qwen/Qwen3.5-0.8B.

Tested two configurations:

Config	TP	DCP	Model
TP4	4	1	`Qwen/Qwen3.5-0.8B`
TP4DCP2	4	2	`Qwen/Qwen3.5-0.8B`
TP4DCP1	4	1	`Qwen/Qwen3.5-397B-A17B-FP8`
TP4DCP2	4	2	`Qwen/Qwen3.5-397B-A17B-FP8`

Test Result

GSM8K smoke accuracy, 256 questions, 5-shot, temperature 0.0:

Config	Accuracy	Correct / Total
TP4DCP1	`0.277344`	71 / 256
TP4DCP2	`0.273438`	70 / 256

GSM8K smoke accuracy for Qwen/Qwen3.5-397B-A17B-FP8, 256 questions, 5-shot:

Config	Accuracy	Correct / Total
TP4DCP1	`0.9140625`	234 / 256
TP4DCP2	`0.91015625`	233 / 256

claude

Claude Code Review

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github-actions · 2026-04-27T09:41:59Z

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Code Review

This pull request introduces support for Decode Context Parallelism (DCP) in hybrid models within the v1 engine, updating the KV cache coordinator, speculative decoding, and CUDA graph capture logic. Key modifications include a supports_dcp flag for layers, batch splitting for DCP execution in FlashAttention, and DCP-aware slot mapping kernels. Review feedback identifies critical bugs in the FlashAttention backend, specifically incorrect slicing of quantization descales by request count rather than token count and a lack of null checks for these descales in non-FP8 models. Furthermore, a typo in vllm/v1/worker/block_table.py using PAD_SLOT_ID instead of PADDING_SLOT_ID must be fixed to avoid runtime failures.

cjackal · 2026-04-27T09:54:03Z

cf. #36480 for the previous work.

I remembered that it had accuracy issue on 397b MoE, wonder if it has been resolved.

Yancey0623 · 2026-04-28T08:41:37Z

cf. #36480 for the previous work.
I remembered that it had accuracy issue on 397b MoE, wonder if it has been resolved.

@cjackal yes, there is no accuracy issue on Qwen3.5-397B-A17B-FP8, and I also updated this test result in the description box.

Config	Accuracy	Correct / Total
TP4DCP1	0.9140625	234 / 256
TP4DCP2	0.91015625	233 / 256

ehfd · 2026-05-08T05:00:16Z

@pisceskkk @LucasWilkinson

ehfd · 2026-05-08T05:00:24Z

@njhill

pisceskkk · 2026-05-13T10:02:20Z

+                    num_decode_tokens,
+                    num_prefill_tokens,
+                ) = self._get_dcp_split_boundaries(attn_metadata)
+                if num_prefills > 0 and num_decodes > 0:


Why do we need to handle the prefill and decode stages separately? Did I miss something?

it's used to split prefill-decode requests in a batch, and after code polish, this pr removed this split, please review the latest codebase, thanks!

mergify · 2026-05-23T09:31:02Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @Yancey0623.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: YanXu <yancey.yx@alibaba-inc.com>

- Move max_num_blocks_per_req sizing into KVCacheSpec subclasses: AttentionSpec applies DCP/PCP token sharding, MambaSpec keeps replicated-state sizing (align mode documents the row-length vs resident-blocks distinction and uses the encoder-aware max_len). Drops the Any-typed helper from cp_utils. - Reinitialize InputBatch when per-group max_num_blocks or slot-mapping modes change, not only block sizes; otherwise the unsharded placeholder sizing silently survives for single-group DCP models. - Reject hybrid KV cache layouts with dcp > 1 unless every group is full-attention or Mamba, restoring fail-closed behavior for e.g. sliding-window hybrid models. - Scope the DCP + speculative-decoding ban to hybrid models; non-hybrid DCP + MTP remains supported. - Keep prefix caching opt-in rather than default-on for hybrid models; the capability is still reported as supported. - Remove the dead CUDA-tensor branch in split_dcp_context_queries. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Jingyi Yang <girasoley@inferact.ai> Signed-off-by: Jingyi Yang <girasoleyang@gmail.com>

The skip-context-attention fast path was gated on this rank's local context length (get_dcp_local_seq_lens with dcp_rank). Contexts shorter than a full interleave round land entirely on a subset of DCP ranks, so ranks with zero local context took the early return in _forward_with_dcp and skipped the query all-gather + LSE-combine collectives while other ranks entered them, deadlocking the DCP group (e.g. first decode after a short prompt, or a short chunked-prefill step). Gate on the global context lengths instead, which are identical on all ranks. Cold pure-prefill batches (global context == 0) still skip; a rank whose local share is empty now runs the context pass with zero seqused_k and contributes -inf LSE, as before this optimization. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: Jingyi Yang <girasoley@inferact.ai> Signed-off-by: Jingyi Yang <girasoleyang@gmail.com>

Signed-off-by: YanXu <yancey.yx@alibaba-inc.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com>

Resolve semantic conflicts with vllm-project#40996 (DCP supports hybrid attention): - keep fine-grained partial-hit machinery; finders still return (blocks, hit_length) and resolve block hashes internally - FullAttentionManager resolves hashes at the DCP/PCP-scaled block size - coordinator eagle margin / FA truncation use the manager's effective (scaled) block size - gate enable_partial_hash_hits on dcp_world_size == 1 Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: zjy0516 <riverclouds.zhu@qq.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com> Signed-off-by: Spicy-Stick <873805887@qq.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com> Signed-off-by: xqchen7 <chenxueqing7@huawei.com>

Signed-off-by: YanXu <yancey.yx@alibaba-inc.com> Signed-off-by: Jingyi Yang <girasoleyang@gmail.com> Co-authored-by: Jingyi Yang <girasoleyang@gmail.com>

noooop · 2026-08-04T07:59:23Z

-                return False
+                logger.debug("Generative hybrid models support prefix caching.")
+                return True
            elif attn_type == "attention_free":


May I ask what is the particular reason for this?

noooop · 2026-08-04T07:59:36Z

-        default_prefix_caching = model_config.is_prefix_caching_supported
+        # Hybrid models support prefix caching but keep it opt-in for now
+        # while the feature matures.
+        default_prefix_caching = (
+            model_config.is_prefix_caching_supported and not model_config.is_hybrid
+        )

        if self.enable_chunked_prefill is None:


May I ask what is the particular reason for this?

Yancey0623 requested review from ApostaC, LucasWilkinson, MatthewBonanni, WoosukKwon, ZJY0516, alexm-redhat, benchislett, heheda12345, luccafong, njhill, orozery, robertgshaw2-redhat, tdoublep, tomeras91, vadiklyutiy and ywang96 as code owners April 27, 2026 09:38

claude Bot reviewed Apr 27, 2026

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mergify Bot added speculative-decoding v1 labels Apr 27, 2026

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Yancey0623 mentioned this pull request May 7, 2026

[Bug]: Decode Context Parallelism (--decode-context-parallel-size) output drift and gibberish in v0.21.0 and latest nightly #41623

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pisceskkk reviewed May 13, 2026

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mergify Bot added the needs-rebase label May 23, 2026

Yancey0623 force-pushed the qwen35_hybrid_attn_dcp branch from 4f5ba69 to 9da2b8b Compare May 27, 2026 10:54

Yancey0623 and others added 4 commits July 8, 2026 21:16

update

5ec0851

Signed-off-by: YanXu <yancey.yx@alibaba-inc.com>

revert mtp

a663710

Signed-off-by: YanXu <yancey.yx@alibaba-inc.com>

Yancey0623 force-pushed the qwen35_hybrid_attn_dcp branch from 23faf73 to a663710 Compare July 8, 2026 13:18

ywang96 merged commit 95ed0fe into vllm-project:main Jul 10, 2026
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github-project-automation Bot moved this to Done in NVIDIA Jul 10, 2026

Yancey0623 deleted the qwen35_hybrid_attn_dcp branch July 10, 2026 06:20

Fangzhou-Ai mentioned this pull request Jul 13, 2026

[Bugfix][ROCm] Keep TP all_gather on base-class collective #48446

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zhao-stack mentioned this pull request Jul 13, 2026

[CI]main2main 0710 vllm-project/vllm-ascend#11709

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zhao-stack mentioned this pull request Jul 14, 2026

[CI] main2mainv0.24 vllm-project/vllm-ascend#11875

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zhcn000000 mentioned this pull request Jul 15, 2026

[Feature]: Can we add base PCP support for hybrid attention? #48747

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[CI] vllm main2main 0730 e5f48df vllm-project/vllm-ascend#13137

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[CI] vllm main2main 0731 0351e9a vllm-project/vllm-ascend#13358

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noooop reviewed Aug 4, 2026

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noooop mentioned this pull request Aug 4, 2026

[Mamba] enable prefix cache by default #50991

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kokomicx mentioned this pull request Aug 13, 2026

[RFC]: Sunset patch_kv_cache_coordinator.py by aligning compressed KV-cache block semantics with vLLM vllm-project/vllm-ascend#14175

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puririshi98 mentioned this pull request Aug 14, 2026

[Backport] Make hybrid prefix caching functional on NemotronH (0% -> 97% hit) TomerBN-Nvidia/vllm#39

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Upstream sync 58/N: merge 2d814a0082 (36 commits, conflict-free) ROCm/vllm#1166

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Upstream sync 59/N: merge 95ed0feaa5 DCP hybrid attention (conflict) ROCm/vllm#1167

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pisceskkk mentioned this pull request Aug 20, 2026

[Misc]feat: adapt to vLLM main (bf2866f8) vllm-project/vllm-ascend#14589

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voipmonitor mentioned this pull request Aug 21, 2026

[II] Merge Kimi-K3 production runtime support local-inference-lab/rtx6kpro#75

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