[DCP][Performance] Add Shared-DCP peer-addressable decode data paths

[foraxe]

[DCP][Performance] Add Shared-DCP peer-addressable decode paths

Depends on #50005.

Summary

This PR introduces Shared-DCP for bounded single-node decode. Shared-DCP represents selected DCP intermediates as peer-addressable distributed GPU objects with stable storage, explicit ownership, local_view / peer_view, and device-side synchronization.

DCP already shards state and combines rank-local results. This PR changes how selected decode intermediates are stored and consumed: producers can write directly into the consumer's final layout, or consumers can load owner shards directly through CUDA VMM peer mappings, without first materializing a complete tensor through a collective.

The PR covers four decode-time communication points:

Boundary	Shared-DCP path
Query AllGather	Fused RoPE/FP8 producers store query shards directly into each consumer-local inbox
Top-K candidate AllGather	Stable-TopK loads owner-local candidate shards directly through local and peer views
LSE AllGather	Output producers store each destination's LSE shard directly into that output owner's VMM allocation
Output ReduceScatter	Output producers store destination head shards into the same owner allocation; the owner performs the final local LSE correction and accumulation

DeepGEMM is unchanged.

Shared-DCP performance vs baseline

The primary full-stack comparison uses the final clean #50009 source for both
arms. The baseline is the current vLLM path with Shared-DCP disabled. The
candidate enables Shared-DCP Query, Top-K, and Output/LSE together.

Workload: GLM-5.2-NVFP4, TP4/DCP4/EP4, c32, 3072 input tokens, 128 output
tokens, 64 prompts, full CUDA graphs, fixed 1800 MHz clocks, and three
alternating fresh-process pairs.

Configuration	TPOT (ms)	Output throughput (tok/s)	p99 ITL (ms)	TTFT (ms)
Baseline	75.526	320.905	392.312	3026.694
Shared-DCP	72.497	331.244	392.526	3031.483
Change	4.01% better	3.22% better	0.05% worse	0.16% worse

Shared-DCP won all three TPOT pairs by 4.122%, 4.105%, and 3.806%. TPOT CV was
0.054% for the baseline and 0.175% for Shared-DCP. Every counted run
completed 64/64 requests.

Output/LSE performance

The updated Output/LSE path publishes each destination head shard directly into owner-local VMM storage. The owner waits for one generation signal per producer and combines only its local receive buffer. This removes both complete LSE materialization and output redistribution from the selected decode operation.

Isolated result versus baseline

This comparison uses the same final source and workload as the full-stack
result. Query and Top-K remain on the baseline paths; only Output/LSE VMM is
enabled.

Configuration	TPOT (ms)	Output throughput (tok/s)	p99 ITL (ms)	TTFT (ms)
Baseline	75.526	320.905	392.312	3026.694
Output/LSE VMM only	73.852	326.585	392.910	3032.196
Change	2.22% better	1.77% better	0.15% worse	0.18% worse

Both rows are three-run means and every run completed 64/64 requests. TPOT CV
was 0.054% for the baseline and 0.745% for Output/LSE VMM.

Comparison with #48897

The focused competitor is a current-base GLM-5.2 port of #48897's direct symmetric-memory A2A path. Both arms used the same model, current base, 4x GB200 hardware, 1800 MHz fixed clocks, TP4/DCP4/EP4 configuration, full CUDA graphs, and A2A handling outside the selected bounded operation.

At the matched Output/LSE operator level, Shared-DCP was 9.61% to 32.49% faster than #48897 across 1 to 64 rows. At the production c32 geometry, latency decreased from 18.384 us to 15.103 us, or 17.85%.

Top-K performance

The Top-K path keeps one candidate shard on each owner and lets the existing
stable-TopK consumer load local and peer shards directly. It removes the
candidate AllGather and avoids constructing a complete local candidate inbox.

Three independent DCP4 processes measured 200 CUDA-graph replays per case at
2048 candidates per rank and 4096 local score columns. Values are median
maximum-rank latency:

Rows	Baseline AllGather (us)	#47348 inbox (us)	Shared-DCP (us)	Shared-DCP vs #47348
1	38.754	41.188	32.596	20.73% faster
8	44.174	41.198	33.022	19.73% faster
32	58.523	47.788	39.336	17.47% faster
64	74.782	53.338	41.138	22.87% faster

This is a 16% to 45% operator improvement over the baseline AllGather and a
17% to 23% improvement over #47348. At the separate full-shape allocation
bound, Shared-DCP uses about 66 MiB/rank instead of #47348's 256 MiB/rank, or
3.88x less physical workspace.

The isolated serving effect is neutral. In the repeated same-revision c32
comparison, TPOT improved by 0.41%, output throughput by 0.35%, p99 ITL by
0.58%, and TTFT by 0.05%. A later logging-fixed same-revision diagnostic was
also effectively flat at 0.074% worse TPOT. These sub-percent results are not
presented as a standalone end-to-end performance win.

Other Shared-DCP performance evidence

• Query producer fanout improved c32 TPOT by 0.816% and output throughput by 0.575% in one same-source point estimate, with p99 ITL and TTFT regressions below 1%.

These per-mechanism results are not assumed additive. The PR does not claim a combined end-to-end gain obtained by summing them.

Memory

At the current bounds, enabling all three paths allocates approximately:

Workspace	Physical allocation per rank
Query consumer inbox	6 MiB
Top-K owner shard	4 MiB
Output/LSE direct-owner buffers, two graph slots	84 MiB
Total	94 MiB

Peer virtual mappings do not duplicate the physical owner allocation.

Validation

Current clean local stack:

reviewed #50009 base: f01c10b225baa0b1fb40405f51b7d759f4840371
clean candidate head: f5c8cc4c1fc716ef3da37eba0a73b6422d28121d
#48897 comparison stack: 67415d20c14dbcb23a9a0a199f71c31d07de8b56

Tests:

tests/distributed/test_dcp_query_vmm.py
tests/distributed/test_dcp_topk_vmm.py
tests/distributed/test_dcp_output_vmm.py
tests/kernels/test_fused_deepseek_v32_norm_rope.py
  -> 54 passed, 14 warnings

Output/LSE coverage includes exact four-rank equivalence, empty and invalid LSE shards, rows 1 through 512, two reusable graph slots, and changing-input CUDA-graph replay.

Applicable Ruff, formatting, mypy, SPDX, policy, and commit checks passed. git diff --check passed.

Relationship to other work

• [Bugfix][DCP] Fix NVIDIA DeepSeek-V3.2 / GLM-5.2 fused attention #50005 is the independent NVIDIA GLM-5.2 DCP correctness prerequisite.
• [Attention] Add direct symmetric-memory DCP A2A #48897 is an open alternative for the Output/LSE boundary using PyTorch symmetric memory and a CUDA direct-A2A implementation. Shared-DCP uses generic CUDA VMM peer objects and also covers Query and Top-K. This PR does not include [Attention] Add direct symmetric-memory DCP A2A #48897 code.
• [DCP][Kernel] Add Shared-DCP consumer-direct Top-K #50010 is the focused Top-K subset of this PR. The overlapping code must not merge twice; it should either land first and be removed here, or be closed as superseded by this complete Shared-DCP PR.
• [Kernel] Optimize CuTeDSL DCP top-k merge #47348 uses a complete symmetric-memory producer inbox for Top-K candidates.
• [DCP] Add FlashInfer fused A2A backend for decode context parallelism #48248 uses FlashInfer MNNVL transport. It remained locally blocked by CUDA_ERROR_NOT_PERMITTED when fallback was disabled, so no local [DCP] Add FlashInfer fused A2A backend for decode context parallelism #48248 performance claim is made.

AI assistance and human review

OpenAI Codex assisted with implementation, validation orchestration, benchmarking, and drafting this description.

• Before updating the PR, the submitter reviewed every changed line and verified the commands and evidence above.

Co-authored with @taoyuanyuan.

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